# loading packages
# devtools::install_github("thomasp85/patchwork")
pacman::p_load(tidyverse, # tidy family and related pacakges below
kableExtra,
gridExtra, # may not use this
purrr,
magrittr, # extending piping
pander, # nice tables
metafor, # package for meta-analysis
MCMCglmm, # Bayeisan mixed model package
ggbeeswarm, # making bee-swarm plots possible
plotly, # interactive plots using ggplot2
MuMIn, # multi-model inference
lme4, # lmm & glmm (models)
broom.mixed, # getting estimates from lmer + glmer objects
performance, # getting R2 from lmer + glmer objects
png, # reading png files
grid, # graphic layout manipulation
patchwork # putting ggplots together
#lmerTest # more functions for lme4
#mi, # missing data analysis
#betareg # dependance of the above
)
# getting functions
source("../R/ESM_functions.R", chdir = TRUE)
We have 5 custom functions named : p_to_Zr(),I2(), R2(), get_est(), get_pred(), and cont_gen(), all of which are used later (see below for their functionality).
Below is the dataset used for our meta-analysis followed by explanations of 24 variables original collected (not all variables were used for our analyses; variables which were neither ‘directly’ nor ‘indirectly’ used in our analyses are indicated by *).
Extended Data Table 1: the meta-analytic dataset of this study.
# getting the data and formating some variables (turning chraracter vectors
# to factors) read the difference between factr() and as.factor()
# <https://stackoverflow.com/questions/39279238/why-use-as-factor-instead-of-just-factor>
# full_data <- read.csv('../data/2019-04-04-source-data-dat.csv', na = 'NA',
# fileEncoding='UTF-8') %>% mutate_if(is.character, as.factor)
full_data <- read_csv("../data/2019-23-07-source-data-dat.csv", na = "NA") %>%
mutate_if(is.character, as.factor)
# making a scrollable table
kable(full_data, "html") %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| authors | year | host_tax_broad | host_tax_fine | symbiont_tax_broad | symbiont_tax_fine | symbiont_euk | symbiosis | endo_or_ecto | mode_of_transmission_broad | mode_of_transmission_fine | symbiont | Visiting_symbiont? | host_tips_linked | host_tips_linked_corrected | host_genera | total_host_symbioint_links | host_range_link_ratio | host_range_taxonomic_breadth | symbiont_tips_linked | symbiont_genera | no_randomizations | p_value | method |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Xu_et_al_2017 | 2017 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Buchnera | resident | 20 | 20 | 8 | 20 | 1.00 | 1.00 | 20 | 1 | 1000 | 0.00100 | TreeMap |
| Riess_et_al_2018 | 2018 | Plant | Fungus | Microbe | Fungus | y | Parasite | Endo | horizontal | autonomous | Root fungus | resident | 11 | 11 | 5 | 11 | 1.00 | 1.00 | 11 | 2 | 1000 | 0.33500 | TreeMap |
| Badets_et_al_2011 | 2011 | Vert | Tetrapod | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Monogenea | resident | 17 | 17 | 13 | 17 | 1.00 | 1.00 | 17 | 4 | 1000 | 0.01000 | TreeMap |
| Banks_et_al_2006 | 2006 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Chewing lice | resident | 18 | 18 | 6 | 30 | 2.00 | 1.60 | 15 | 2 | 1000 | 0.01000 | TreeMap |
| Bochkov_et_al_2011 | 2011 | Vert | Tetrapod | Invert | Invert | y | Parasite | Ecto | both | contact | Mites | resident | 6 | 6 | NA | 9 | 1.00 | 1.00 | 9 | 6 | 100 | 0.01000 | TreeMap |
| Charleston_&_Perkins_2003 | 2003 | Vert | Tetrapod | Microbe | Protist | y | Parasite | Endo | horizontal | vector | Malaria | resident | 9 | 9 | 1 | 9 | 1.00 | 1.00 | 9 | 1 | 100 | 0.03000 | TreeMap |
| Charleston_&_Robertson_2002 | 2002 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 12 | 12 | 12 | 12 | 1.00 | 1.00 | 12 | 1 | 1000 | 0.01500 | TreeMap |
| Chauvatcharin_et_al_2006 | 2006 | Microbe | Bacterium | Microbe | Virus | n | Parasite | Endo | both | NA | Bacteriophage | resident | 14 | 14 | 7 | 15 | 1.07 | 1.00 | 14 | 1 | 1000 | 0.33100 | TreeMap |
| Clark_et_al_2000 | 2000 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Buchnera | resident | 17 | 17 | 4 | 17 | 1.00 | 1.00 | 17 | 1 | 1000 | 0.00100 | TreeMap |
| Clayton_&_Johnson_2003 | 2003 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Dove body lice | resident | 13 | 13 | 6 | 14 | 1.08 | 1.15 | 13 | 1 | 10000 | 0.00060 | TreeMap |
| Clayton_&_Johnson_2003 | 2003 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Dove wing lice | resident | 13 | 13 | 6 | 16 | 1.60 | 1.50 | 10 | 1 | 10000 | 0.15300 | TreeMap |
| Cui_et_al_2012 | 2012 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 7 | 7 | 3 | 7 | 1.00 | 1.00 | 7 | 7 | 10000 | 0.36600 | TreeMap |
| Dabert_2001__ | 2001 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Feather mite | resident | 21 | 21 | 12 | 22 | 1.00 | 1.00 | 22 | 9 | 10000 | 0.00100 | TreeMap |
| Deng_et_al_2013 | 2013 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Parasitic wasps | resident | 7 | 7 | 4 | 10 | 1.00 | 1.43 | 10 | 2 | 999 | 0.53000 | TreeMap |
| Desai_et_al_2010 | 2010 | Microbe | Protist | Microbe | Bacterium | n | Mutualist | Ecto | both | contact | Devescovinid flagellates | resident | 9 | 9 | 2 | 8 | 0.89 | 1.00 | 9 | 1 | 1000 | 0.00100 | TreeMap |
| Desdevises_et_al_2002 | 2002 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Monogenea | resident | 14 | 14 | 11 | 39 | 1.95 | 1.45 | 20 | 2 | 999 | 0.31700 | TreeMap |
| Downie_&_Gullan_2005 | 2005 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Primary endosymbiont Tremblaya | resident | 21 | 21 | 16 | 21 | 1.00 | 1.00 | 21 | 1 | 1000 | 0.00100 | TreeMap |
| Erpenbeck_et_al_2002 | 2002 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Sponge symbiont | resident | 6 | 6 | 5 | 6 | 1.00 | 1.00 | 6 | 1 | 10000 | 0.02000 | TreeMap |
| Etherington_et_al_2006 | 2006 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Calicivirus | resident | 7 | 7 | 7 | 8 | 1.00 | 1.00 | 8 | 1 | 1000 | 0.01000 | TreeMap |
| Farrell_1998 | 1998 | Plant | Plant | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Cerambicid beetle | resident | 21 | 21 | 2 | 21 | 1.00 | 1.00 | 21 | 6 | 1000 | 0.07000 | TreeMap |
| Gottschling_et_al_2011 | 2011 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Papillomavirus | resident | 43 | 43 | 40 | 78 | 1.00 | 1.00 | 78 | 30 | 1000 | 0.00100 | TreeMap |
| Hendricks_et_al_2013 | 2013 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Quill mites | resident | 19 | 19 | 17 | 20 | 1.25 | 1.25 | 16 | 1 | 1000 | 0.02100 | TreeMap |
| Hosokawa_et_al_2006 | 2006 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Gamma proteobacteria | resident | 7 | 7 | 3 | 7 | 1.00 | 1.00 | 7 | 1 | 1000 | 0.00100 | TreeMap |
| Hugot_1999 | 1999 | Vert | Tetrapod | Invert | Invert | y | Parasite | Endo | horizontal | trophic | Pinworms | resident | 10 | 10 | 9 | 11 | 1.00 | 1.00 | 11 | 6 | 1000 | 0.00100 | TreeMap |
| Hugot_et_al_2003 | 2003 | Vert | Tetrapod | Microbe | Fungus | y | Parasite | Endo | horizontal | contact | Pneumocystis | resident | 19 | 19 | 12 | 19 | 1.00 | 1.00 | 19 | 1 | 1000 | 0.00100 | TreeMap |
| Huyse_&_Volckaert_2005 | 2005 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Gyrodactylus flatworms | resident | 8 | 8 | 3 | 22 | 1.29 | 1.24 | 17 | 1 | 100 | 0.02000 | TreeMap |
| IkedaOhtsubo_&_Brune_2009 | 2009 | Microbe | Protist | Microbe | Bacterium | n | Mutualist | Ecto | vertical | vertical | Endomicrobium | resident | 11 | 11 | 1 | 11 | 1.00 | 1.00 | 11 | 1 | 1000 | 0.00100 | TreeMap |
| Jackson_&_Charleston_1994 | 1994 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Lyssavirus | resident | 10 | 10 | 8 | 10 | 1.00 | 1.00 | 10 | 1 | 100 | 0.19000 | TreeMap |
| Jackson_&_Charleston_1994 | 1994 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 10 | 10 | 7 | 10 | 1.00 | 1.00 | 10 | 1 | 100 | 0.01000 | TreeMap |
| Jackson_&_Charleston_1994 | 1994 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Arenavirus | resident | 11 | 11 | 7 | 12 | 1.00 | 1.00 | 12 | 1 | 100 | 0.05000 | TreeMap |
| Jackson_&_Charleston_1994 | 1994 | Vert | Bird | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 13 | 13 | 9 | 15 | 1.00 | 1.00 | 15 | 1 | 100 | 0.18000 | TreeMap |
| Jackson_&_Charleston_1994 | 1994 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Hantavirus | resident | 14 | 14 | 7 | 17 | 1.00 | 1.00 | 17 | 1 | 100 | 0.01000 | TreeMap |
| Jeong_et_al_1999 | 1999 | Plant | Plant | Microbe | Bacterium | n | Mutualist | Endo | horizontal | vector | Frankia | resident | 12 | 12 | 12 | 19 | 1.06 | 1.00 | 18 | 1 | 1000 | 0.23000 | TreeMap |
| Johnson_et_al_2002 | 2002 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Bird louse | resident | 25 | 25 | 23 | 25 | 1.32 | 1.42 | 19 | 19 | 1000 | 0.23000 | TreeMap |
| Johnson_et_al_2003 | 2003 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Dove wing lice | resident | 28 | 28 | 15 | 31 | 1.48 | 1.33 | 21 | 1 | 100 | 0.03000 | TreeMap |
| Johnson_et_al_2006 | 2006 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Flamingo lice | resident | 10 | 10 | NA | 11 | 1.00 | 1.00 | 11 | 11 | 1000 | 0.40200 | TreeMap |
| Jousselin_et_al_2008 | 2009 | Plant | Plant | Invert | Invert | y | Mutualist | Endo | horizontal | autonomous | Fig wasp | resident | 15 | 15 | 1 | 15 | 1.07 | 1.07 | 14 | 6 | 10000 | 0.01000 | TreeMap |
| Jousselin_et_al_2008 | 2009 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Fig wasp | resident | 13 | 13 | 1 | 14 | 1.00 | 1.00 | 14 | 1 | 10000 | 0.01000 | TreeMap |
| Jousselin_et_al_2008 | 2009 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Fig wasp | resident | 13 | 13 | 1 | 13 | 1.00 | 1.00 | 13 | 1 | 10000 | 0.01000 | TreeMap |
| Jousselin_et_al_2008 | 2009 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Fig wasp | resident | 16 | 16 | 1 | 18 | 1.06 | 1.06 | 17 | 2 | 10000 | 0.01000 | TreeMap |
| Jousselin_et_al_2009 | 2009 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Buchnera | resident | 55 | 22 | 1 | 22 | 1.00 | 1.00 | 22 | 1 | 10000 | 0.00100 | TreeMap |
| Kawaida_et_al_2013 | 2013 | Invert | Invert | Plant | Plant | y | Mutualist | Endo | vertical | vertical | Green algae | resident | 6 | 6 | 1 | 6 | 1.00 | 1.00 | 6 | 1 | 10000 | 0.00350 | TreeMap |
| Kawakita_et_al_2004 | 2004 | Plant | Plant | Invert | Invert | y | Mutualist | Ecto | horizontal | autonomous | Pollinating moth | visitor | 18 | 18 | 1 | 18 | 1.00 | 1.00 | 18 | 1 | 999 | 0.01900 | TreeMap |
| Kelley_&_Farrell_1998 | 1998 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Bark beetle | resident | 41 | 41 | 1 | 89 | 6.85 | 1.92 | 13 | 1 | 100 | 0.28000 | TreeMap |
| Kikuchi_et_al_2009 | 2009 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Gut symbiont | resident | 14 | 14 | 5 | 14 | 1.00 | 1.00 | 14 | 1 | 1000 | 0.00100 | TreeMap |
| Lanterbecq_et_al_2010 | 2010 | Invert | Invert | Invert | Invert | y | Parasite | Endo/Ecto | horizontal | contact | Myzostomid worm | resident | 16 | 16 | 12 | 16 | 1.00 | 1.00 | 16 | 5 | 5000 | 0.04000 | TreeMap |
| Light_&_Hafner_2008 | 2008 | Vert | Tetrapod | Invert | Invert | y | Parasite | Ecto | both | contact | Rodent sucking lice | resident | 44 | 21 | 4 | 21 | 1.00 | 1.00 | 21 | 1 | 1000 | 0.00100 | TreeMap |
| LimFong_et_al_2008 | 2008 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Endobugula bacteria | resident | 5 | 5 | 1 | 5 | 1.00 | 1.00 | 5 | 2 | 1000 | 0.11000 | TreeMap |
| Liu_et_al_2013 | 2013 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Buchnera | resident | 37 | 37 | 1 | 37 | 1.00 | 1.00 | 37 | 1 | 1000 | 0.00100 | TreeMap |
| Liu_et_al_2014 | 2014 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Buchnera | resident | 27 | 27 | 3 | 29 | 1.00 | 1.00 | 29 | 1 | 1000 | 0.01000 | TreeMap |
| LopezVaamonde_et_al_2001 | 2001 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Figwasp | resident | 15 | 15 | 1 | 15 | 1.00 | 1.00 | 15 | 1 | 1000 | 0.00100 | TreeMap |
| LopezVaamonde_et_al_2003 | 2003 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Leaf mining moth | resident | 33 | 33 | 33 | 77 | 1.00 | 1.00 | 77 | 1 | 1000 | 0.21300 | TreeMap |
| LopezVaamonde_et_al_2005 | 2005 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Parasitic wasp | resident | 28 | 28 | 1 | 35 | 2.33 | 1.33 | 15 | 1 | 1000 | 0.24800 | TreeMap |
| Martin_et_al_1999 | 1999 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 38 | 38 | 38 | 48 | 1.00 | 1.00 | 48 | 1 | 1000 | 0.00100 | TreeMap |
| Martin_et_al_2003 | 2003 | Vert | Bird | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 14 | 14 | 14 | 16 | 1.00 | 1.00 | 16 | 1 | 100 | 0.01000 | TreeMap |
| Martin_et_al_2003 | 2003 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 9 | 9 | 9 | 9 | 1.00 | 1.00 | 9 | 1 | 100 | 0.05000 | TreeMap |
| Martin_et_al_2003 | 2003 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 17 | 17 | 17 | 23 | 1.00 | 1.00 | 23 | 1 | 100 | 0.21000 | TreeMap |
| Mazzon_et_al_2010 | 2010 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Stammerula | resident | 17 | 17 | 10 | 17 | 1.06 | 1.13 | 16 | 3 | 1000 | 0.00100 | TreeMap |
| Morelli_&_Spicer_2007 | 2007 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Bird nasal mite | resident | 6 | 6 | 6 | 6 | 1.00 | 1.00 | 6 | 1 | 10000 | 0.00950 | TreeMap |
| Muniz_et_al_2013 | 2013 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 17 | 17 | 11 | 17 | 1.00 | 1.00 | 17 | 1 | 10000 | 0.00001 | TreeMap |
| Musser_et_al_2010 | 2010 | Vert | Mammal | Invert | Invert | y | Parasite | Ecto | both | contact | Squirrel sucking lice | resident | 6 | 6 | 1 | 6 | 1.00 | 1.00 | 6 | 6 | 100 | 0.30000 | TreeMap |
| Pagan_et_al_2010 | 2010 | Plant | Plant | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Tobamovirus | resident | 10 | 10 | 10 | 13 | 1.00 | 1.00 | 13 | 1 | 1000 | 0.24000 | TreeMap |
| Page_1996 | 1996 | Vert | Tetrapod | Invert | Invert | y | Parasite | Ecto | both | contact | Gopher chewing lice | resident | 15 | 15 | 6 | 17 | 1.00 | 1.00 | 17 | 2 | 1000 | 0.00100 | TreeMap |
| Page_et_al_1998 | 1998 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Bird lice | resident | 7 | 7 | 1 | 8 | 1.00 | 1.00 | 8 | 1 | 100 | 0.01000 | TreeMap |
| Page_et_al_2004 | 2004 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Seabird lice | resident | 12 | 12 | 4 | 12 | 1.00 | 1.00 | 12 | 1 | 1000 | 0.00100 | TreeMap |
| Page_et_al_2004 | 2004 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Seabird lice | resident | 11 | 11 | 5 | 13 | 1.00 | 1.00 | 13 | 4 | 100 | 0.25000 | TreeMap |
| Page_et_al_2004 | 2004 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Seabird lice | resident | 13 | 13 | 5 | 14 | 1.00 | 1.00 | 14 | 1 | 100 | 0.46000 | TreeMap |
| Page_et_al_2004 | 2004 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Seabird lice | resident | 9 | 9 | 3 | 9 | 1.00 | 1.00 | 9 | 1 | 100 | 0.36000 | TreeMap |
| Paterson_&_Poulin_1999 | 1999 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Parasitic copepod | resident | 8 | 8 | 8 | 12 | 1.20 | 2.20 | 10 | 1 | 100 | 0.01000 | TreeMap |
| Paterson_et_al_2000 | 2000 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Seabird lice | resident | 11 | 11 | 5 | 14 | 1.00 | 1.00 | 14 | 5 | 100 | 0.01000 | TreeMap |
| Percy_et_al_2004 | 2004 | Plant | Plant | Invert | Invert | y | Parasite | Ecto | both | autonomous | Psyllid | resident | 35 | 35 | 8 | 56 | 1.22 | 1.09 | 46 | 4 | 1000 | 0.00500 | TreeMap |
| PerezLosada_et_al_2006 | 2006 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | NA | Polyomavirus | resident | 9 | 9 | 9 | 11 | 1.00 | 1.00 | 11 | 1 | 100 | 0.01000 | TreeMap |
| Perlman_et_al_2003 | 2003 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Nematode | resident | 16 | 16 | 1 | 17 | 1.89 | 1.33 | 9 | 1 | 1000 | 0.14000 | TreeMap |
| Quek_et_al_2004 | 2004 | Plant | Plant | Invert | Invert | y | Mutualist | Ecto | horizontal | autonomous | Ants | resident | 11 | 11 | 1 | 23 | 2.30 | 1.60 | 10 | 1 | 1000 | 0.99900 | TreeMap |
| Ramsden_et_al_2008 | 2008 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Hantavirus | resident | 33 | 33 | 20 | 38 | 1.00 | 1.00 | 38 | 1 | 1000 | 0.99900 | TreeMap |
| Reed_et_al_2007 | 2007 | Vert | Tetrapod | Invert | Invert | y | Parasite | Ecto | horizontal | contact | Anthropoid lice | resident | 4 | 4 | 4 | 6 | 1.20 | 1.00 | 5 | 4 | 1000 | 0.05000 | TreeMap |
| Refregier_et_al_2008 | 2008 | Plant | Plant | Microbe | Fungus | y | Parasite | Endo | horizontal | vector | Anther smut fungi | resident | 18 | 18 | 7 | 20 | 1.25 | 1.25 | 16 | 1 | 3000 | 0.50000 | TreeMap |
| Shoemaker_et_al_2002 | 2002 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | both | NA | Wolbachia | resident | 20 | 20 | 7 | 23 | 1.00 | 1.00 | 23 | 1 | 100 | 0.47000 | TreeMap |
| Simkova_et_al_2013 | 2013 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Monogenea | resident | 5 | 5 | 1 | 21 | 1.00 | 1.00 | 21 | 1 | 999 | 0.04500 | TreeMap |
| Six_&_Paine_1999 | 1999 | Microbe | Fungus | Invert | Invert | y | Mutualist | Ecto | horizontal | vector | Mycangial fungi | resident | 6 | 6 | 1 | 6 | 1.00 | 1.00 | 6 | 3 | 1000 | 0.03100 | TreeMap |
| Skerikova_et_al_2001 | 2001 | Vert | Fish | Invert | Invert | y | Parasite | Endo | horizontal | trophic | Cestode | resident | 7 | 7 | 7 | 7 | 1.00 | 1.00 | 7 | 1 | 10000 | 0.40000 | TreeMap |
| Smith_et_al_2008b | 2008 | Vert | Tetrapod | Invert | Invert | y | Parasite | Ecto | both | contact | Rodent lice | resident | 20 | 20 | 3 | 20 | 1.00 | 1.00 | 20 | 14 | 100 | 0.05000 | TreeMap |
| Sorenson_et_al_2004 | 2004 | Vert | Bird | Vert | Bird | y | Parasite | Ecto | horizontal | autonomous | Brood parasitic finch | resident | 33 | 33 | 10 | 34 | 1.62 | 1.43 | 21 | 1 | 100 | 0.15000 | TreeMap |
| Subbotin_et_al_2004 | 2004 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Nematode | resident | 16 | 16 | 16 | 21 | 1.00 | 1.00 | 21 | 4 | 1000 | 0.00100 | TreeMap |
| Switzer_et_al_2005 | 2005 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Retrovirus | resident | 46 | 46 | 17 | 51 | 1.00 | 1.00 | 51 | 1 | 10000 | 0.00700 | TreeMap |
| Urban_&_Cryan_2012 | 2012 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | autonomous | Fulgorid planthopper microbe | resident | 40 | 40 | 38 | 40 | 1.00 | 1.00 | 40 | 1 | 1000 | 0.00100 | TreeMap |
| Urban_&_Cryan_2012 | 2012 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | autonomous | Fulgorid planthopper microbe | resident | 30 | 30 | 29 | 30 | 1.00 | 1.00 | 30 | 1 | 1000 | 0.00100 | TreeMap |
| Vanhove_et_al_2015 | 2015 | Vert | Fish | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Monogenea | resident | 19 | 19 | 10 | 28 | 1.00 | 1.00 | 28 | 1 | 10000 | 0.04210 | TreeMap |
| Weckstein_2004 | 2004 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Toucan chewing lice | resident | 11 | 11 | 1 | 11 | 2.20 | 1.80 | 5 | 1 | 10000 | 0.89000 | TreeMap |
| Weiblen_&_Bush_2002 | 2002 | Plant | Plant | Invert | Invert | y | Mutualist | Endo | horizontal | autonomous | Fig wasp | resident | 19 | 19 | 1 | 19 | 1.00 | 1.00 | 19 | 1 | 10000 | 0.01950 | TreeMap |
| Weiblen_&_Bush_2002 | 2002 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Fig wasp | resident | 12 | 12 | 1 | 18 | 1.00 | 1.00 | 18 | 1 | 10000 | 0.12150 | TreeMap |
| Wu_et_al_2008 | 2008 | Plant | Plant | Microbe | Virus | n | Parasite | Endo | horizontal | vector | Mastrevirus | resident | 8 | 8 | 8 | 10 | 1.00 | 1.00 | 10 | 1 | 100 | 0.01000 | TreeMap |
| Yan_et_al_2011 | 2011 | Vert | Fish | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Lymphocystis | resident | 8 | 8 | 8 | 15 | 1.00 | 1.00 | 15 | 1 | 999 | 0.98900 | TreeMap |
| Riess_et_al_2018 | 2018 | Plant | Fungus | Microbe | Fungus | y | Parasite | Endo | horizontal | autonomous | Root fungus | resident | 11 | 11 | 5 | 11 | 1.00 | 1.00 | 11 | 2 | 9999 | 0.26400 | Parafit |
| Souza_et_al_2018 | 2018 | Vert | Mammal | Microbe | Virus | n | Parasite | Endo | both | bodily fluid | Primate hepadnaviruses | resident | 8 | 8 | 8 | 19 | 1.06 | 1.00 | 18 | 1 | 999 | 0.00500 | Parafit |
| Ramasindrazana_et_al_2017 | 2017 | Vert | Mammal | Invert | Invert | y | Parasite | Ecto | both | contact | Bat flies (Nycteribiidae) | resident | 15 | 15 | 6 | 26 | 2.89 | 1.78 | 9 | 5 | 999 | 0.00100 | Parafit |
| Li_et_al_2017 | 2017 | Plant | Plant | Microbe | Fungus | y | Parasite | Endo | horizontal | vector | Grass fungus (Tranzscheliella) | Resident | 12 | 12 | 9 | 12 | 1.71 | 2.00 | 7 | 1 | 999 | 0.50505 | Parafit |
| Sweet_et_al_2018b | 2018 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | body lice (Physconelloides) | resident | 11 | 11 | 4 | 13 | 1.86 | 1.43 | 7 | 1 | 100000 | 0.00500 | Parafit |
| Sweet_&_Johnson_2018 | 2018 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | wing lice (Columbicola) | resident | 13 | 13 | 4 | 14 | 2.80 | 1.80 | 5 | 1 | 100000 | 0.00500 | Parafit |
| Arab_et_al_2019 | 2019 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Blattabacterium of cockroaches | resident | 55 | 55 | 52 | 55 | 1.00 | 1.00 | 55 | 1 | 999 | 0.00100 | Parafit |
| Sweet_et_al_2018a | 2018 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Brueelia bird lice | resident | 259 | 259 | 163 | 283 | 1.63 | 1.61 | 174 | 11 | 9999 | 0.00010 | Parafit |
| Hewitt_et_al_2019 | 2019 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Unionid mussels | resident | 178 | 178 | 79 | 495 | 7.17 | 3.30 | 69 | 35 | 999 | 0.00100 | Parafit |
| Latinne_et_al_2018 | 2018 | Vert | Mammal | Microbe | Funus | y | Parasite | Endo | horizontal | enviromental | Pneumocystis of rodents | resident | 15 | 15 | 6 | 19 | 3.17 | 2.00 | 6 | 1 | 999 | 0.00900 | Parafit |
| Graca_et_al_2018 | 2018 | Vert | Fish | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Monogenea | resident | 9 | 9 | 7 | 18 | 1.29 | 1.29 | 14 | 1 | 10000 | 0.00010 | Parafit |
| Sweet_et_al_2017 | 2017 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Phabine bird lice | resident | 12 | 12 | 5 | 15 | 1.15 | 1.23 | 13 | 3 | 999 | 0.06900 | Parafit |
| Zhang_et_al_2017 | 2017 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Nardonella of Weevils | resident | 44 | 44 | 24 | 44 | 1.00 | 1.00 | 44 | 1 | 100000 | 0.00001 | Parafit |
| Megia-palma_et_al_2018 | 2018 | Vert | Reptile | Microbe | Protist | y | Parasite | Endo | horizontal | vector | Lizard Schellackia Apicomplexa | resident | 16 | 16 | 8 | 23 | 1.53 | 1.27 | 15 | 8 | 999 | 0.00400 | Parafit |
| Dona_2018 | 2018 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Trouessartia Feather mite | resident | 14 | 14 | 13 | 15 | 1.00 | 1.00 | 15 | 1 | 100000 | 0.01000 | Parafit |
| Dona_2018 | 2018 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Proctophyllodes Feather mite | resident | 42 | 42 | 29 | 44 | 1.00 | 1.00 | 44 | 1 | 100000 | 0.01000 | Parafit |
| Xu_et_el_2017 | 2017 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Buchner | resident | 20 | 20 | 8 | 20 | 1.00 | 1.00 | 20 | 1 | 999 | 0.00100 | Parafit |
| Chen_et_al_2017 | 2017 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Buchnera | resident | 50 | 50 | 11 | 50 | 1.00 | 1.00 | 50 | 1 | 999 | 0.00100 | Parafit |
| Patra_et_al_2018 | 2018 | Vert | Fish | Invert | Invert | y | Parasite | Endo | horizontal | environmental | Myxozoa | resident | 24 | 24 | 24 | 31 | 1.00 | 1.00 | 31 | 1 | 999 | 0.00100 | Parafit |
| Matthews_et_al_2018 | 2018 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Amerodectes feather mites | resident | 12 | 12 | 6 | 33 | 1.00 | 0.58 | 33 | 1 | 99900 | 0.00400 | Parafit |
| Catanach_et_al_2018 | 2018 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Colpocephalum | resident | 54 | 28 | 5 | 44 | 1.07 | 0.93 | 41 | 30 | 999 | 0.00100 | Parafit |
| Ballinger_et_al_2018 | 2018 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Spiroplasma | resident | 11 | 11 | 1 | 12 | 1.00 | 1.00 | 12 | 1 | 999 | 0.07000 | Parafit |
| Li_et_al_2018 | 2018 | Vert | Vert | Invert | Invert | y | Parasite | Endo | horizontal | trophic | Ascarid worms | resident | 68 | 68 | 34 | 129 | 1.45 | NA | 89 | 89 | 100000 | 0.00100 | Parafit |
| Holzer_et_al_2018 | 2018 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | environmental | Myxozoa | resident | 23 | 23 | 22 | 39 | 1.00 | 1.00 | 39 | 21 | 1000 | 0.00100 | Parafit |
| Holzer_et_al_2018 | 2018 | Vert | Fish | Invert | Invert | y | Parasite | Endo | horizontal | environmental | Myxozoa FW (OIM) | resident | 69 | 69 | 62 | 101 | 1.00 | 1.00 | 101 | 15 | 1000 | 0.00100 | Parafit |
| Holzer_et_al_2018 | 2018 | Vert | Fish | Invert | Invert | y | Parasite | Endo | horizontal | environmental | Myxozoa (PIM) | resident | 69 | 69 | 58 | 75 | 1.00 | 1.00 | 75 | 21 | 1000 | 0.00100 | Parafit |
| Carneiro_et_al_2018 | 2018 | Vert | Mammal | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Hepatovirus | resident | 26 | 26 | 21 | 26 | 1.00 | 1.00 | 26 | 1 | 100000 | 0.01000 | Parafit |
| Jesovnik_et_al_2017 | 2017 | Invert | Invert | Microbe | Fungus | y | Mutualist | Ecto | both | environmental | Ant fungus | resident | 32 | 6 | 1 | 6 | 1.00 | 1.00 | 6 | 1 | 999 | 0.02900 | Parafit |
| Singh_et_al_2017 | 2017 | Microbe | Fungus | Plant | Plant | y | Mutualist | Endo | vertical | vertical | Lichen algae | resident | 23 | 17 | 1 | 25 | 1.25 | 1.15 | 20 | 1 | 9999 | 0.00020 | Parafit |
| Endara_et_al_2018 | 2018 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Sawfly | resident | 44 | 30 | 1 | 45 | 1.18 | 1.03 | 38 | NA | 9999 | 0.01500 | Parafit |
| Endara_et_al_2017 | 2017 | Plant | Plant | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Gelechioidea moths | visitor | 18 | 18 | 1 | 29 | 1.00 | 2.17 | 29 | NA | 100 | 0.70000 | Parafit |
| Endara_et_al_2017 | 2017 | Plant | Plant | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Riodinidae moths | visitor | 10 | 10 | 1 | 18 | 1.50 | 1.25 | 12 | NA | 100 | 0.90000 | Parafit |
| Endara_et_al_2017 | 2017 | Plant | Plant | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Erebidae moths | visitor | 14 | 14 | 1 | 18 | 1.20 | 1.13 | 15 | NA | 100 | 0.74000 | Parafit |
| Lauber_et_al_2017 | 2017 | Vert | Vert | Microbe | Virus | n | Parasite | Endo | horizontal | autonomous | Nackednaviruses and Hepadnaviruses | resident | 30 | 30 | 28 | 30 | 0.88 | 1.00 | 34 | 8 | 10000 | 0.00010 | Parafit |
| Althoff_et_al_2012 | 2012 | Plant | Plant | Invert | Invert | y | Mutualist | Ecto | horizontal | autonomous | Yucca moth | visitor | 24 | 24 | 1 | 40 | 2.00 | 1.35 | 20 | 1 | 1000 | 0.00100 | Parafit |
| Althoff_et_al_2012 | 2012 | Plant | Plant | Invert | Invert | y | Mutualist | Ecto | horizontal | autonomous | Yucca moth | visitor | 24 | 24 | 1 | 38 | 2.24 | 1.53 | 17 | 1 | 1000 | 0.00100 | Parafit |
| Banks_et_al_2006 | 2006 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Penguin chewing lice | resident | 18 | 18 | 6 | 30 | 2.00 | 1.60 | 15 | 2 | 10000 | 0.00100 | Parafit |
| Bayerlova_2009 | 2009 | Vert | Mammal | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Arenavirus | resident | 21 | 21 | 14 | 31 | 1.55 | 1.45 | 20 | 1 | 9999 | 0.00040 | Parafit |
| Bellec_et_al_2014 | 2014 | Plant | Plant | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Prasinovirus | resident | 22 | 22 | 3 | 133 | 2.61 | 1.65 | 51 | 1 | 999 | 0.00100 | Parafit |
| Bruyndonckxx_et_al_2009 | 2009 | Vert | Mammal | Invert | Invert | y | Parasite | Ecto | both | contact | Bat mites | resident | 20 | 20 | 7 | 21 | 1.91 | 2.27 | 11 | 2 | 9999 | 0.00300 | Parafit |
| Caraguel_et_al__2007 | 2007 | Microbe | Amoeba | Microbe | Protist | y | Mutualist | Endo | vertical | vertical | Prokinetoplastid endosymbiont | resident | 6 | 6 | 1 | 6 | 1.00 | 1.00 | 6 | 1 | 9999 | 0.00100 | Parafit |
| Choi_&_Thines_2015 | 2015 | Plant | Plant | Microbe | Fungus | y | Parasite | Endo | horizontal | autonomous | Downy mildew | resident | 63 | 63 | 28 | 63 | 1.00 | 1.00 | 63 | 3 | 999 | 0.00100 | Parafit |
| Conord_et_al_2008 | 2008 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Dryophthoridae endosymbionts | resident | 14 | 14 | 10 | 14 | 1.00 | 1.00 | 14 | 1 | 999 | 0.00900 | Parafit |
| Cornuault_et_al_2012 | 2012 | Vert | Bird | Microbe | Protist | y | Parasite | Endo | horizontal | vector | Leucocytozoon | resident | 8 | 8 | 1 | 23 | 1.28 | 1.17 | 18 | 1 | 9999 | 0.03500 | Parafit |
| Cruaud_et_al_2012 | 2012 | Plant | Plant | Invert | Invert | y | Mutualist | Endo | horizontal | autonomous | Fig wasp | resident | 200 | 200 | 1 | 200 | 1.00 | 1.00 | 200 | 20 | 9999 | 0.01000 | Parafit |
| Cui_et_al_2014 | 2014 | Vert | Bird | Microbe | Virus | n | Parasite | Endo | horizontal | bodily fluid | Hepadnavirus | resident | 46 | 46 | 46 | NA | NA | NA | 61 | NA | 99999 | 0.23300 | Parafit |
| Deng_et_al_2013 | 2013 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Parasitic wasp | resident | 7 | 7 | 4 | 10 | 1.00 | 1.00 | 10 | 2 | 999 | 0.01602 | Parafit |
| Desdevises_et_al_2002 | 2002 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Monogenea | resident | 14 | 14 | 11 | 39 | 2.00 | 1.65 | 20 | 2 | 999 | 0.26000 | Parafit |
| Dhami_et_al_2013 | 2013 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Hoataupuhia symbiont | resident | 42 | 42 | NA | 42 | 1.00 | 1.00 | 42 | 4 | 999 | 0.00100 | Parafit |
| Dowie_et_al_2016 | 2016 | Microbe | Fungus | Plant | Plant | y | Parasite | Endo | horizontal | environmental | Parasitic plant | resident | 4 | 4 | 1 | 9 | 2.25 | 1.50 | 4 | 1 | 9999 | 0.00100 | Parafit |
| Du_Toit_et_al_2013 | 2013 | Vert | Mammal | Invert | Invert | y | Parasite | Ecto | both | contact | Rodent lice | resident | 4 | 4 | 1 | 14 | 1.17 | 1.17 | 12 | 1 | 10000 | 0.88000 | Parafit |
| FerrerParis_et_al_2013 | 2013 | Plant | Plant | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Butterflies | visitor | 64 | 64 | NA | 112 | 2.73 | 5.20 | 41 | NA | 999 | 0.15700 | Parafit |
| FraijaFernandez_et_al_2016 | 2016 | Vert | Mammal | Invert | Invert | y | Parasite | Endo | horizontal | trophic | Digenea | resident | 31 | 31 | 24 | 50 | 5.56 | 3.67 | 9 | 6 | 999 | 0.00100 | Parafit |
| Garamszegi_2009 | 2009 | Vert | Mammal | Microbe | Protist | y | Parasite | Endo | horizontal | vector | Primate malaria | resident | 23 | 23 | 23 | 43 | 2.39 | 2.56 | 18 | 1 | 1000 | 0.00100 | Parafit |
| Garcia_&_Hayman_2016 | 2016 | Vert | Vert | Microbe | Protist | y | Parasite | Endo | horizontal | trophic | Cryptosporidium | resident | 22 | 22 | 22 | 36 | 1.33 | 1.96 | 27 | 1 | 999 | 0.01000 | Parafit |
| Gavotte_et_al_2007 | 2007 | Microbe | Bacterium | Microbe | Virus | n | Parasite | Endo | both | NA | Bacteriophage | resident | 33 | 33 | 1 | 51 | 0.93 | 1.00 | 55 | 1 | 10000 | 0.13190 | Parafit |
| Goker_et_al_2011 | 2011 | Microbe | Fungus | Microbe | Virus | n | Parasite | Endo | NA | NA | Mycovirus | resident | 8 | 8 | 8 | 8 | 1.00 | 1.00 | 8 | 1 | 9999 | 0.09780 | Parafit |
| Gomard_et_al_2016 | 2016 | Vert | Mammal | Microbe | Bacterium | n | Parasite | Endo | horizontal | NA | Leptospira | resident | 12 | 12 | 11 | 26 | 1.00 | 1.00 | 26 | 1 | 999 | 0.09000 | Parafit |
| Gottschling_et_al_2011 | 2011 | Vert | Mammal | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Papillomavirus | resident | 43 | 43 | 40 | 76 | 0.97 | 1.00 | 78 | 30 | 9999 | 0.00010 | Parafit |
| Hall_et_al__2016 | 2016 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Carsonella primary endosymbiont | resident | 37 | 37 | 18 | 37 | 1.00 | 1.00 | 37 | 1 | 10000 | 0.00100 | Parafit |
| Hall_et_al__2016 | 2016 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Arsenophonus secondary endosymbiont | resident | 20 | 20 | 9 | 20 | 1.00 | 1.00 | 20 | 1 | 10000 | 0.38700 | Parafit |
| Hammer_et_al_2010 | 2010 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Petrel chewing lice | resident | 23 | 23 | 6 | 23 | 1.10 | 1.00 | 21 | 1 | 999 | 0.00100 | Parafit |
| Hammerlinck_et_al_2016 | 2016 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Parasitic wasp | resident | 11 | 11 | 2 | 11 | 1.38 | 1.13 | 8 | 2 | 999 | 0.12900 | Parafit |
| Hammerlinck_et_al_2016 | 2016 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Parasitic wasp | resident | 6 | 6 | 2 | 6 | 1.50 | 1.25 | 4 | 2 | 999 | 0.19500 | Parafit |
| Hammerlinck_et_al_2016 | 2016 | Invert | Invert | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Parasitic wasp | resident | 7 | 7 | 2 | 7 | 1.00 | 1.00 | 7 | 2 | 999 | 0.04200 | Parafit |
| Hembry_et_al_2013 | 2013 | Plant | Plant | Invert | Invert | y | Mutualist | Ecto | horizontal | autonomous | Pollinating moths | visitor | 37 | 37 | 1 | 35 | 1.00 | 1.00 | 35 | 1 | 999 | 0.01700 | Parafit |
| Herrera__et_al_2016 | 2016 | Microbe | Fungus | Microbe | Fungus | y | Parasite | Endo | horizontal | environmental | Mycoparasite Cosmospora | resident | 13 | 13 | 9 | 13 | 1.00 | 1.00 | 13 | 3 | 999 | 0.00500 | Parafit |
| Hoglund_et_al_2003 | 2003 | Vert | Vert | Invert | Invert | y | Parasite | Endo | horizontal | trophic | Lungworm | resident | 8 | 8 | 8 | 10 | 2.00 | 2.40 | 5 | 1 | 10000 | 0.09800 | Parafit |
| Hughes_et_al_2007 | 2007 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Pelican lice | resident | 18 | 18 | 6 | 18 | 1.06 | 1.00 | 17 | 1 | 999 | 0.00010 | Parafit |
| Huyse_&_Volckaert_2005 | 2005 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Monogenea | resident | 8 | 8 | 5 | 22 | 1.29 | 1.29 | 17 | 1 | 999 | 0.09500 | Parafit |
| Irwin_et_al_2012 | 2012 | Vert | Mammal | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Arenavirus | resident | 21 | 21 | 14 | 31 | 1.55 | 1.85 | 20 | NA | 9999 | 0.00040 | Parafit |
| Jenkins_et_al_2012 | 2012 | Vert | Bird | Microbe | Protist | y | Parasite | Endo | horizontal | autonomous | Leucocytozoon | resident | 52 | 52 | 40 | 138 | 1.45 | NA | 95 | 1 | 999 | 0.00100 | Parafit |
| Jousselin_et_al_2008 | 2008 | Plant | Plant | Invert | Invert | y | Mutualist | Endo | horizontal | autonomous | Fig wasp | resident | 15 | 15 | 1 | 15 | 1.07 | 1.07 | 14 | 6 | 9999 | 0.00300 | Parafit |
| Jousselin_et_al_2008 | 2008 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Fig wasp | resident | 13 | 13 | 1 | 13 | 1.00 | 1.00 | 13 | 1 | 9999 | 0.02000 | Parafit |
| Jousselin_et_al_2008 | 2008 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Fig wasp | resident | 16 | 16 | 1 | 18 | 1.06 | 1.06 | 17 | 2 | 9999 | 0.00100 | Parafit |
| Jousselin_et_al_2008 | 2008 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Fig wasp | resident | 13 | 13 | 1 | 14 | 1.00 | 1.00 | 14 | 1 | 9999 | 0.00300 | Parafit |
| Jousselin_et_al_2009 | 2009 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Buchnera | resident | 55 | 22 | 1 | 22 | 1.00 | 1.00 | 22 | 1 | 9999 | 0.00100 | Parafit |
| Kaltenpoth_et_al_2014 | 2014 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Beewolf bacterial symbiont | resident | 39 | 39 | 2 | 41 | 1.00 | 1.00 | 41 | 1 | 1000 | 0.00100 | Parafit |
| Kawakita_&_Kato_2009 | 2009 | Plant | Plant | Invert | Invert | y | Mutualist | Ecto | horizontal | autonomous | Yucca moth | visitor | 10 | 10 | 7 | 10 | 1.00 | 1.00 | 10 | 1 | 100 | 0.39000 | Parafit |
| Kawakita_et_al_2004 | 2004 | Plant | Plant | Invert | Invert | y | Mutualist | Ecto | horizontal | autonomous | Pollinating moth | visitor | 18 | 18 | 1 | 18 | 1.00 | 1.00 | 18 | 1 | 999 | 0.00500 | Parafit |
| Kawazoe_et_al_2008 | 2008 | Invert | Invert | Invert | Invert | y | Mutualist | Ecto | vertical | vertical | Bee mite | resident | 4 | 4 | 1 | 5 | 1.25 | 1.25 | 4 | 1 | 9999 | 0.00010 | Parafit |
| Kolsch_&__Pedersen_2010 | 2010 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Reed beetle bacterial endosymbiont | resident | 41 | 41 | 7 | 41 | 1.00 | 1.00 | 41 | 1 | 9999 | 0.00100 | Parafit |
| Krasnov_&_Shenbrot_2013 | 2013 | Vert | Mammal | Invert | Invert | y | Parasite | Ecto | horizontal | contact | Jerboa fleas | resident | 21 | 21 | 8 | 62 | 3.26 | 4.37 | 19 | 7 | 999 | 0.16000 | Parafit |
| Krumbholz_et_al_2009 | 2009 | Vert | Vert | Microbe | Virus | n | Parasite | Endo | horizontal | NA | Polyomavirus | resident | 13 | 13 | 13 | 18 | 1.00 | 1.00 | 18 | 1 | 999999 | 0.49460 | Parafit |
| Ku_&_Hu_2014 | 2014 | Plant | Plant | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Burkholderia plant symbiont | resident | 11 | 11 | 1 | 11 | 1.00 | 1.00 | 11 | 1 | 9999 | 0.02000 | Parafit |
| Lanterbecq_et_al_2010 | 2010 | Invert | Invert | Invert | Invert | y | Parasite | Endo/Ecto | horizontal | contact | Myzostomid worm | resident | 16 | 16 | 12 | 16 | 1.00 | 1.00 | 16 | 5 | 1000 | 0.01300 | Parafit |
| Lauron_et_al_2015 | 2015 | Vert | Bird | Microbe | Protist | y | Parasite | Endo | horizontal | vector | Avian malaria | resident | 18 | 18 | 8 | 83 | 1.80 | NA | 46 | 1 | 1000 | 0.66000 | Parafit |
| Lei_&_Olival_2014 | 2014 | Vert | Mammal | Microbe | Bacterium | n | Parasite | Endo | horizontal | environmental | New World bat Bartonella | resident | 14 | 14 | 11 | 38 | 1.00 | 1.00 | 38 | 1 | 999 | 0.00100 | Parafit |
| Lei_&_Olival_2014 | 2014 | Vert | Mammal | Microbe | Bacterium | n | Parasite | Endo | horizontal | environmental | New World rodent Bartonella | resident | 4 | 4 | 4 | 20 | 1.00 | 1.00 | 20 | 1 | 999 | 0.00100 | Parafit |
| Lei_&_Olival_2014 | 2014 | Vert | Mammal | Microbe | Bacterium | n | Parasite | Endo | horizontal | environmental | New World bat Leptospira | resident | 14 | 14 | 12 | 19 | 1.00 | 1.00 | 19 | 1 | 999 | 0.85800 | Parafit |
| Lei_&_Olival_2014 | 2014 | Vert | Mammal | Microbe | Bacterium | n | Parasite | Endo | horizontal | environmental | Old World bat Bartonella | resident | 9 | 9 | 8 | 13 | 1.00 | 1.00 | 13 | 1 | 999 | 0.02900 | Parafit |
| Lei_&_Olival_2014 | 2014 | Vert | Mammal | Microbe | Bacterium | n | Parasite | Endo | horizontal | environmental | Old World rodent Bartonella | resident | 35 | 35 | 22 | 119 | 1.09 | 1.17 | 109 | 1 | 999 | 0.00010 | Parafit |
| Lei_&_Olival_2014 | 2014 | Vert | Mammal | Microbe | Bacterium | n | Parasite | Endo | horizontal | environmental | Old World bat Leptospira | resident | 6 | 6 | 5 | 7 | 1.00 | 1.00 | 7 | 1 | 999 | 0.75870 | Parafit |
| LewisRogers_&_Crandall_2009 | 2009 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | contact | Picornaviridae | resident | 6 | 6 | NA | 27 | 1.00 | 1.00 | 27 | 11 | 999 | 0.47000 | Parafit |
| Li_et_al_2017 | 2017 | Plant | Plant | Microbe | Fungus | y | Parasite | Endo | horizontal | autonomous | Smut fungi | resident | 12 | 12 | 9 | 12 | 1.71 | 1.57 | 7 | 1 | 999 | 0.50505 | Parafit |
| Light_&_Hafner_2008 | 2008 | Vert | Mammal | Invert | Invert | y | Parasite | Ecto | both | contact | Rodent sucking lice | resident | 44 | 21 | 4 | 21 | 1.00 | 1.00 | 21 | 1 | 999 | 0.00100 | Parafit |
| Liu_et_al_2016 | 2016 | Plant | Plant | Microbe | Fungus | y | Parasite | Endo | NA | environmental | Tree foliar fungi | resident | 13 | 13 | 10 | 44 | 1.57 | 2.71 | 28 | 28 | 9999 | 0.02510 | Parafit |
| Liu_et_al_2016 | 2016 | Plant | Plant | Microbe | Fungus | y | Parasite | Endo | NA | environmental | Subtropical tree soil fungi | resident | 19 | 19 | 16 | 76 | 3.30 | 3.22 | 23 | 23 | 9999 | 0.02030 | Parafit |
| Maneesakorn_et_al_2011 | 2011 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Photorhabdus symbiotic bacteria in nematodes | resident | 12 | 12 | 1 | 12 | 1.00 | 1.00 | 12 | 1 | 999 | 0.00100 | Parafit |
| Martinez_et_al_2011 | 2011 | Invert | Invert | Microbe | Protist | y | Parasite | Endo | horizontal | vector | Avian malaria | resident | 16 | 16 | 1 | 22 | 2.44 | 1.56 | 9 | 1 | 999 | 0.07500 | Parafit |
| Martinez_San_udo_&_Girolami_2009 | 2009 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Tephritid symbiotic bacteria | resident | 19 | 19 | 10 | 19 | 1.12 | 1.18 | 17 | 5 | 999 | 0.00300 | Parafit |
| Mattiucci_&_nascetti_2008 | 2008 | Vert | Mammal | Invert | Invert | y | Parasite | Endo | horizontal | trophic | Nematode | resident | 7 | 7 | 15 | 12 | 1.33 | 1.78 | 9 | 1 | 100 | 0.05000 | Parafit |
| Mazzon_et_al_2010 | 2010 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Stammerula bacterial symbiont | resident | 17 | 17 | 10 | 17 | 1.06 | 1.00 | 16 | 3 | 999 | 0.00700 | Parafit |
| McFrederick_&_Taylor_2013 | 2013 | Invert | Invert | Invert | Invert | y | NA | Ecto | vertical | vertical | Nematode | resident | 7 | 7 | 3 | 7 | 1.00 | 1.00 | 7 | 1 | 10000 | 0.00600 | Parafit |
| McKee_et_al_2016 | 2016 | Vert | Mammal | Microbe | Bacterium | n | Parasite | Endo | horizontal | vector | Bat bartonella bacteria | resident | 66 | 66 | 42 | 184 | 1.06 | 1.09 | 173 | 1 | 10000 | 0.00010 | Parafit |
| McLeish_&_Van_Noort_2012 | 2012 | Plant | Plant | Invert | Invert | y | Mutualist | Endo | horizontal | autonomous | Fig wasp | resident | 26 | 26 | 1 | 65 | 1.00 | 1.00 | 65 | 6 | 9999 | 0.18000 | Parafit |
| Mehdiabadi_et_al_2012 | 2012 | Invert | Invert | Microbe | Fungus | y | Mutualist | Ecto | both | environmental | Ant fungus | resident | 99 | 11 | 1 | 11 | 1.00 | 1.00 | 11 | 1 | 999 | 0.00100 | Parafit |
| Mendlova_et_al_2012 | 2012 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Monogenea | resident | 6 | 6 | 5 | 34 | 1.17 | 1.21 | 29 | 2 | 999 | 0.02400 | Parafit |
| Merville_et_al._2013 | 2013 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Curculioniphilus buchneri primary endosymbiont of weevils | resident | 9 | 9 | 1 | 9 | 1.00 | 1.00 | 9 | 1 | 999 | 0.00350 | Parafit |
| Millanes_et_al_2014 | 2014 | Microbe | Fungus | Microbe | Fungus | y | Parasite | Endo | both | environmental | Biatoropsis fungus | resident | 16 | 16 | 2 | 16 | 1.00 | 1.00 | 16 | 1 | 999 | 0.23600 | Parafit |
| Miyaki_et_al_2016 | 2016 | Vert | Fish | Microbe | Bacterium | n | Mutualist | Endo | horizontal | environmental | Giant bacteria Epulopiscium | resident | 8 | 8 | 4 | 54 | 3.18 | 2.24 | 17 | 1 | 999 | 0.00100 | Parafit |
| Mondo_et_al_2012 | 2012 | Microbe | Fungus | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Glomeribacter gigasporarum fungal symbiont | resident | 55 | 5 | 2 | 5 | 1.00 | 1.00 | 5 | 1 | 10000 | 0.00010 | Parafit |
| Nouioui_et_al_2014 | 2014 | Plant | Plant | Microbe | Bacterium | n | Mutualist | Endo | horizontal | vector | Frankia | resident | 9 | 9 | 1 | 20 | 1.00 | 1.00 | 20 | 1 | 9999 | 0.33000 | Parafit |
| Pellissier_et_al_2013 | 2013 | Plant | Plant | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Butterflies | visitor | 104 | 104 | NA | NA | NA | NA | 97 | NA | 10000 | 0.00010 | Parafit |
| Perkins_2010 | 2010 | Vert | Fish | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Monogenea | resident | 61 | 61 | 50 | 75 | 1.00 | 1.00 | 75 | NA | 9999 | 0.92600 | Parafit |
| Peterson_et_al_2010 | 2010 | Plant | Plant | Microbe | Fungus | y | Parasite | Endo | horizontal | environmental | Beech fungus | resident | 11 | 11 | 1 | 25 | 2.08 | 1.58 | 12 | 1 | 9999 | 0.00010 | Parafit |
| Polme_et_al_2014.pdf | 2014 | Plant | Plant | Microbe | Bacterium | n | Mutualist | Endo | horizontal | vector | Frankia | resident | 22 | 22 | 1 | NA | NA | NA | 43 | 1 | 999 | 0.00100 | Parafit |
| Quek_et_al_2004 | 2004 | Plant | Plant | Invert | Invert | y | Mutualist | Ecto | horizontal | autonomous | Ant | resident | 10 | 10 | 1 | 23 | 2.30 | 1.70 | 10 | 1 | 999 | 0.77900 | Parafit |
| Ricklefs_et_al_2004 | 2004 | Vert | Bird | Microbe | Protist | y | Parasite | Endo | horizontal | vector | Avian malaria | resident | 44 | 44 | 1 | 121 | 1.86 | NA | 65 | 2 | 100 | 0.63000 | Parafit |
| Savio_2011 | 2011 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | horizontal | NA | Erwinia dacicola tephritid symbiont | resident | 17 | 17 | 10 | 17 | 1.00 | 1.00 | 17 | 3 | 999 | 0.00700 | Parafit |
| Schardl_et_al_2008 | 2008 | Plant | Plant | Microbe | Fungus | y | Mutualist | Endo | both | NA | Grass endophytic fungi (epichloae) | resident | 25 | 25 | 16 | 25 | 0.96 | 1.00 | 26 | 2 | 1000 | 0.00100 | Parafit |
| Sibbald_et_al_2017 | 2017 | Microbe | Amoeba | Microbe | Protist | y | Mutualist | Endo | horizontal | environmental | Paramoeba | resident | 7 | 7 | 1 | 7 | 1.00 | 1.00 | 7 | 1 | 9999 | 0.00010 | Parafit |
| Simkova_et_al_2013 | 2013 | Vert | Fish | Invert | Invert | y | Parasite | Ecto | horizontal | autonomous | Monogenea | resident | 5 | 5 | 1 | 21 | 1.00 | 1.00 | 21 | 1 | 999 | 0.01300 | Parafit |
| Singh_et_al_2016 | 2016 | Microbe | Fungus | Plant | Plant | y | Mutualist | Endo | horizontal | environmental | Trebouxia algae | resident | 23 | 23 | 1 | 28 | 1.40 | 1.20 | 20 | 1 | 9999 | 0.00020 | Parafit |
| Sontowski_et_al_2015 | 2015 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Wolbachia | resident | 14 | 14 | 4 | 14 | 1.00 | 1.00 | 14 | 1 | 100000 | 0.00300 | Parafit |
| Sorenson_et_al_2004 | 2004 | Vert | Bird | Vert | Bird | y | Parasite | Ecto | horizontal | autonomous | Brood parasitic finches | resident | 33 | 33 | 10 | 34 | 1.62 | 1.43 | 21 | 1 | 1000 | 0.00100 | Parafit |
| Stireman_et_al_2010 | 2010 | Plant | Plant | Invert | Invert | y | Parasite | Endo | horizontal | autonomous | Gall midge | resident | 15 | 15 | 10 | 39 | 1.39 | 1.25 | 28 | 1 | 9999 | 0.00080 | Parafit |
| Sudakaran_et_al_2015 | 2015 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | NA | NA | Gordonibacter | resident | 16 | 16 | 7 | 22 | 1.00 | 1.00 | 22 | 1 | 1000 | 0.97800 | Parafit |
| Sudakaran_et_al_2015 | 2015 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | NA | NA | Coriobacterium glomerans | resident | 14 | 14 | 6 | 26 | 1.00 | 1.00 | 26 | 1 | 1000 | 0.97400 | Parafit |
| Summers_&_Rouse_2014 | 2014 | Invert | Invert | Invert | Invert | y | Parasite | Endo/Ecto | horizontal | autonomous | Myzostomida | resident | 53 | 53 | 36 | 78 | 1.13 | 1.23 | 69 | 10 | 9999 | 0.00050 | Parafit |
| Susoy_&_Herrmann_2014 | 2014 | Invert | Invert | Invert | Invert | y | Mutualist | Ecto | vertical | vertical | Nematode | resident | 35 | 35 | 7 | 37 | 1.42 | 1.31 | 26 | 1 | 9999 | 0.00010 | Parafit |
| Swafford_&_Bond_2010 | 2010 | Invert | Invert | Invert | Invert | y | Parasite | Ecto | horizontal | contact | Millipede mite | resident | 7 | 7 | 1 | 7 | 1.00 | 1.00 | 7 | 1 | 9999 | 0.31900 | Parafit |
| Sweet_et_al_2016 | 2016 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Dove wing lice | resident | 52 | 52 | 25 | 57 | 1.33 | 1.21 | 43 | 1 | 100000 | 0.00001 | Parafit |
| Sweet_et_al_2016 | 2016 | Vert | Bird | Invert | Invert | y | Parasite | Ecto | both | contact | Dove body lice | resident | 52 | 52 | 25 | 58 | 1.18 | 1.14 | 49 | 4 | 100000 | 0.00001 | Parafit |
| Tao_et_al_2013 | 2013 | Vert | Tetrapod | Microbe | Virus | n | Parasite | Endo | horizontal | NA | Polyomavirus | resident | 7 | 7 | 7 | 10 | 1.00 | 1.00 | 10 | 1 | 99999 | 0.17592 | Parafit |
| Toju_et_al_2013 | 2013 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Curculioniphilus | resident | 27 | 27 | 4 | 27 | 1.00 | 1.00 | 27 | 1 | 99999 | 0.00001 | Parafit |
| Urban_&_Cryan_2012 | 2012 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Fulgoroidea associated Betaproteobacteria | resident | 30 | 30 | 29 | 30 | 1.00 | 1.00 | 30 | 1 | 1000 | 0.00100 | Parafit |
| Urban_&_Cryan_2012 | 2012 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Fulgoroidea associated Sulcia | resident | 40 | 40 | 38 | 40 | 1.00 | 1.00 | 40 | 1 | 1000 | 0.00300 | Parafit |
| Viale_et_al_2015 | 2015 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | vertical | vertical | Fruit fly Stammerula | resident | 23 | 23 | 1 | 23 | 1.00 | 1.00 | 23 | 1 | 9999 | 0.00100 | Parafit |
| Won_et_al_2008 | 2008 | Invert | Invert | Microbe | Bacterium | n | Mutualist | Endo | horizontal | contact | Mussel thiotroph symbiont | resident | 15 | 15 | 5 | 15 | 1.00 | 1.00 | 15 | 1 | 100 | 0.37000 | Parafit |
A. authors: The authors of the study and the date (citation form).
B. year: Year of publication of the study.
C. host_tax_broad: Separation of the host group according to broader taxonomic units (e.g. vertebrate, invertebrate, microbe, plant).
D. host_tax_fine*: Separation of the host group according to narrower taxonomic units (e.g. fish, tetrapod, bird, invertebrate, protist, bacterium, plant, fungus).
E. symbiont_tax_broad: Separation of the symbiont group according to broader taxonomic units (e.g. vertebrate, invertebrate, microbe, plant).
F. symbiont_tax_fine*: Separation of the symbiont group according to narrower taxonomic units (e.g. invertebrate, protist, virus, bacterium, fungus, plant, bird).
G. symbiont_euk*: Whether the symbiont is eukaryotic (state =‘yes’), or prokaryotic (state=‘no’).
H. symbiosis: The type of symbiont (e.g. parasite or mutualist). For this we followed the definition used by the authors of the study.
I. endo_or_ecto: Whether the symbiont lives outside the host (i.e. is an ectosymbiont), or inside the host (i.e. is an endosymbiont).
J. mode_of_transmission_broad: Whether the symbiont is transmitted vertically, horizontally, or both. For this, we followed the route of transmission specified by the authors of the study
K. mode_of_transmission_fine*: A finer-scale description of the mode of transmission of the symbiont (e.g. contact, vector, bodily fluid, vertical, trophic).
L. symbiont*: Shorthand description of the type of symbiont.
M. Visiting_symbiont?* Whether the symbiont is resident on the host (resident), or makes visits to the host or hosts (visitor).
N. host_tips_linked: The number of individual host taxa included in the cophylogenetic analysis.
O. host_tips_linked_corrected The same measure as for column N, ‘host_tips_linked’, but reduced to only include one member of each host species. This is included because some authors include multiple individuals of the same host species. Without correction, this artificially increases the apparent number of host species included in the study.
P. host_genera: A count of the number of host genera included in the cophylogenetic analysis
Q. total_host_symbioint_links The total number of links between host and symbiont taxa recorded in a study. If all symbionts were strict specialists, this would equal the number of symbionts included in the study. However, because symbionts are often associated with more than one host, this value is often higher than the total number of symbionts included in the study.
R. host_range_link_ratio: An estimation of symbiont host specificity, calculated by dividing the total number of links between hosts and symbionts (i.e. ‘total_host_symbiont_links’, column Q), by the total number of symbionts included in the study (i.e. ‘symbiont_tips_linked’, column T).
S. host_range_taxonomic_breadth: An alternative estimation of symbiont host specificity, calculated by first summing the number of host taxonomic ranks linked to each symbiont (i.e. single host species = 1, multiple host species in the same genera = 2, multiple host genera = 3, multiple host families = 4, multiple host orders = 5), and dividing by the total number of symbionts included in the study (i.e. ‘symbiont_tips_linked’, column T)
T. symbiont_tips_linked The number of individual symbiont taxa included in the cophylogenetic analysis.
U. symbiont_genera: A count of the number of symbiont genera included in the cophylogenetic analysis.
V. no_randomizations: The number of phylogenetic randomizations performed during the cophylogenetic analysis.
W. p_value: The p-value reported for the cophylogenetic analysis, representing the likelihood that host and symbiont phylogenies display cospeciation.
X. method: Whether TreeMap or ParaFit was used to obtain co-divergence or a p value.
We present the number of sample size for two separate methods: TreeMap1 and ParaFit2 (and combined) for effect sizes, papers and different levels of categorical variables (factors).
# selecting out variables, which we used for our analysis
dat <- full_data %>% select(-host_tax_fine, -symbiont_tax_fine, -symbiont_euk, -mode_of_transmission_fine, -symbiont, -`Visiting_symbiont?`)
# making a table of sample sizes for different variables
dat %>% group_by(method) %>%
summarise(
`Effect sizes` = n(),
Papers = n_distinct(authors),
`Vertebrate hosts` = sum(host_tax_broad == "Vert", na.rm = T), # na.rm is important when NA exists
`Invertebrate hosts` = sum(host_tax_broad == "Invert", na.rm = T),
`Plant hosts` = sum(host_tax_broad == "Plant", na.rm = T),
`Microbe hosts` = sum(host_tax_broad == "Microbe", na.rm = T),
`Vertebrate symbionts` = sum(symbiont_tax_broad == "Vert", na.rm = T),
`Invertebrate symbionts` = sum(symbiont_tax_broad == "Invert", na.rm = T),
`Plant symbionts` = sum(symbiont_tax_broad == "Plant", na.rm = T),
`Microbe symbionts` = sum(symbiont_tax_broad == "Microbe", na.rm = T),
`Parastic relationships` = sum(symbiosis == "Parasite", na.rm = T),
`Mutualistic relatioships` = sum(symbiosis == "Mutualist", na.rm = T),
`Ecto-symbionts` = sum(endo_or_ecto == "Ecto", na.rm = T),
`Endo-symbionts` = sum(endo_or_ecto == "Endo", na.rm = T),
`Ecto/endo-symbionts` = sum(endo_or_ecto == "Endo/Ecto", na.rm = T),
`Horizontal transmission` = sum(mode_of_transmission_broad == "horizontal", na.rm = T),
`Vertical transmission` = sum(mode_of_transmission_broad == "vertical", na.rm = T),
`Horizontal/vertical-transmission` = sum(mode_of_transmission_broad == "both", na.rm = T)
) -> n_table1
# transposing the table and creating that table and adding a correct number of the papers for `Combined`
n_authors <- n_distinct(dat$authors) # the total number of papers
n_table2 <-t(n_table1[,-1])
colnames(n_table2) <- n_table1$method
n_table2 %>% as_tibble(rownames = "Number") %>%
mutate(Combined = Parafit + TreeMap, Combined = replace(Combined, 2, n_authors)) %>%
rename("Number of" = "Number", "ParaFit (n)" = "Parafit", "TreeMap (n)" = "TreeMap", "Combined (n)" = "Combined") %>%
kable() %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "250px")
| Number of | ParaFit (n) | TreeMap (n) | Combined (n) |
|---|---|---|---|
| Effect sizes | 140 | 93 | 233 |
| Papers | 118 | 78 | 180 |
| Vertebrate hosts | 60 | 51 | 111 |
| Invertebrate hosts | 39 | 20 | 59 |
| Plant hosts | 31 | 18 | 49 |
| Microbe hosts | 10 | 4 | 14 |
| Vertebrate symbionts | 1 | 1 | 2 |
| Invertebrate symbionts | 62 | 49 | 111 |
| Plant symbionts | 3 | 1 | 4 |
| Microbe symbionts | 74 | 42 | 116 |
| Parastic relationships | 91 | 70 | 161 |
| Mutualistic relatioships | 48 | 23 | 71 |
| Ecto-symbionts | 41 | 34 | 75 |
| Endo-symbionts | 97 | 58 | 155 |
| Ecto/endo-symbionts | 2 | 1 | 3 |
| Horizontal transmission | 84 | 53 | 137 |
| Vertical transmission | 28 | 15 | 43 |
| Horizontal/vertical-transmission | 23 | 25 | 48 |
#pander(split.cell = 40, split.table = Inf) # not as nice as kable
Note that for the numbers of papers do not add up (TreeMap + ParaFit \(\neq\) Combined) because 16 papers used both TreeMap and ParaFit methods (the term “papers” here is our variable auhtors)
Below, we have the number of missing data (cells) for all the variables used in the analysis.
# summaring missingness in our dataset
# funs(sum(is.na(.))) needs to be in funs as is.na has "." = each column
dat %>% summarise_all(~sum(is.na(.))) %>% # map(~sum(is.na(.)) # this is an alterantive way
t() %>% as_tibble(rownames = "Variable") %>%
rename("Number of missing data (n)" = "V1") %>%
#pander(split.cell = 40, split.table = Inf)
kable() %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "60%", height = "250px")
| Variable | Number of missing data (n) |
|---|---|
| authors | 0 |
| year | 0 |
| host_tax_broad | 0 |
| symbiont_tax_broad | 0 |
| symbiosis | 1 |
| endo_or_ecto | 0 |
| mode_of_transmission_broad | 5 |
| host_tips_linked | 0 |
| host_tips_linked_corrected | 0 |
| host_genera | 6 |
| total_host_symbioint_links | 3 |
| host_range_link_ratio | 3 |
| host_range_taxonomic_breadth | 7 |
| symbiont_tips_linked | 0 |
| symbiont_genera | 9 |
| no_randomizations | 0 |
| p_value | 0 |
| method | 0 |
# an alternative method using the mi package
#missing_data_tbl <- missing_data.frame(as.data.frame(data))
#show(missing_data_tbl)
Here, we created our effect size (correlation coefficient r and its Fisher’s z transformation Zr) from p values and associated sample sizes3. We used the sum of host_tips_linked_corrected and symbiont_tips_linked as our sample size (i.e., the number of both host and symbiont species) for each effect size (an indicator of congruence). Also, we created a column with unique ID for each observation (i.e. an observation level random effect), termed observation, which is required for the rma.mv function in metafor4.
dat %<>% # getting sample size & observation level random effect
mutate(., sample_size = host_tips_linked_corrected + symbiont_tips_linked, observation = factor(1:nrow(.)))
# calcuating effect size somehow it did not run by piping with above (not
# recognising the first aurgument)
dat %<>% p_to_Zr(p_value, sample_size)
First, we checked what random effects should be put into the main model to do this we fitted two random effects, authors (i.e. study IDs) and observation; the former term was added to account for non-independence of effect sizes originating from the same papers (i.e., authors).
# 2 random effects & model AIC note that probably only base stuff works
# outside of main chunck so need to create AIC here
ma_test1 <- rma.mv(yi = Zr, V = VZr, random = list(~1 | authors, ~1 | observation),
data = dat)
aic1 <- AIC(ma_test1)
# 1 random effect & model AIC
ma_test2 <- rma.mv(yi = Zr, V = VZr, random = ~1 | authors, data = dat)
aic2 <- AIC(ma_test2)
The model (ma_test1), which included both random factors, had a larger AIC value (39.91) than the model with only one random effect (37.91) . This is because observation hardly accounted any variance (< 0.0001) compared to authors (0.0327). Therefore, we only had authors as our random factor in subsequent analyses.
We ran intercept models (meta-analyses) with 3 different datasets (ParaFit, TreeMap and both combined; see the explanation of method above). Also, we note that we used adjustments for test statistics and confidence intervals (test = "t"), which is similar to (but not the same as) those proposed by Kanpp and Hartung5; probably this approach is a more conservative approach.
# think about making this into a tibble meta-analysis with Parafit
ma_parafit <- rma.mv(yi = Zr, V = VZr, random = ~1 | authors, test = "t", subset = which(method ==
"TreeMap"), data = dat)
# meta-analysis with TreeMap
ma_treemap <- rma.mv(yi = Zr, V = VZr, random = ~1 | authors, test = "t", subset = which(method ==
"Parafit"), data = dat)
# meta-analysis with all the data combined
ma_all <- rma.mv(yi = Zr, V = VZr, test = "t", random = ~1 | authors, data = dat)
Supplementary Table 1: Overall effects (meta-analytic means), 95% confidence intervals (CIs), variance components (V) and heterogeneity, I2 (I2)6 from metafor model using the 3 datasets (ParaFit, TreeMap and both combined, or All). Note that in these models, I2[total] = I2[authors] (see7,8), as we only have one random factor.
# getting I2 for the models could use map()
i2_treemap <- I2(ma_treemap)
i2_parafit <- I2(ma_parafit)
i2_all <- I2(ma_all)
# creating a table
tibble(Dataset = c("Parafit", "TreeMap", "All"), `Overall mean (Zr)` = c(ma_parafit$b,
ma_treemap$b, ma_all$b), `Lower CI [0.025]` = c(ma_parafit$ci.lb, ma_treemap$ci.lb,
ma_all$ci.lb), `Upper C [0.975]` = c(ma_parafit$ci.ub, ma_treemap$ci.ub,
ma_all$ci.ub), `V[authors]` = c(ma_parafit$sigma2, ma_treemap$sigma2, ma_all$sigma2),
`I2[total]` = c(i2_parafit[1], i2_treemap[1], i2_all[1])) %>% kable("html",
digits = 3) %>% kable_styling("striped", position = "left")
| Dataset | Overall mean (Zr) | Lower CI [0.025] | Upper C [0.975] | V[authors] | I2[total] |
|---|---|---|---|---|---|
| Parafit | 0.333 | 0.273 | 0.394 | 0.036 | 0.523 |
| TreeMap | 0.350 | 0.308 | 0.392 | 0.028 | 0.600 |
| All | 0.345 | 0.309 | 0.381 | 0.033 | 0.590 |
These models gave all consistent results including heterogeneity. Given these results, we proceeded with only analyzing the whole dataset (All) from this on.
# https://stackoverflow.com/questions/41919023/ggplot-adding-image-on-top-right-in-two-plots-with-different-scales
# how to add png files to the figure (above) reading image
image_mutualism <- readPNG("../images/mutualism_transparentbg.png")
image_parasitism <- readPNG("../images/parasitism_transparentbg.png")
# creating a table of results
pred_ma <- get_pred(ma_all)
effect_ma <- get_est(ma_all) %>% left_join(pred_ma)
# creating a forest plot
fig_ma <- ggplot(data = effect_ma, aes(x = tanh(estimate), y = "Overall mean")) +
scale_x_continuous(limits = c(-1, 1), breaks = seq(-1, 1, by = 0.2)) + geom_quasirandom(data = dat,
aes(x = tanh(Zr), y = "Overall mean", size = (1/VZr) + 3), groupOnX = FALSE,
alpha = 0.2) + # precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0,
show.legend = F, size = 0.5, alpha = 0.6) + # CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0,
show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) + # creating dots and different size (bee-swarm and bubbles)
geom_point(size = 3, shape = 21, fill = "black") + annotate("text", x = 0.93,
y = 1.15, label = paste("italic(k)==", length(dat$Zr)), parse = TRUE, hjust = "left",
size = 3.5) + labs(x = expression(paste(italic(r), " (correlation)")), y = "",
size = expression(paste(italic(n), " (# of species)"))) + theme_bw() + theme(legend.position = c(0,
1), legend.justification = c(0, 1)) + theme(legend.direction = "horizontal") +
# theme(legend.background = element_rect(fill = 'white', colour = 'black'))
# +
theme(legend.background = element_blank()) + theme(axis.text.y = element_text(size = 10,
colour = "black", hjust = 0.5, angle = 90)) + annotation_custom(rasterGrob(image_mutualism),
xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) + annotation_custom(rasterGrob(image_parasitism),
xmin = -0.9, xmax = -0.7, ymin = 0.6, ymax = 1.2)
# ggsave(plot = fig_ma, filename = 'fig_2a.pdf', height = 2, width = 8)
# ggploty 0 does not work (Error in unique.default(x) : unimplemented type
# 'expression' in 'HashTableSetup')
fig_ma
# for Fig 3
a <- ggplot(data = effect_ma, aes(x = tanh(estimate), y = "Overall mean")) +
scale_x_continuous(limits = c(-1, 1), breaks = seq(-1, 1, by = 0.2)) + geom_quasirandom(data = dat,
aes(x = tanh(Zr), y = "Overall mean", size = (1/VZr) + 3), groupOnX = FALSE,
alpha = 0.2) + # precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0,
show.legend = F, size = 0.5, alpha = 0.6) + # CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0,
show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) + # creating dots and different size (bee-swarm and bubbles)
geom_point(size = 3, shape = 21, fill = "black") + annotate("text", x = 0.93,
y = 1.15, label = paste("italic(k)==", length(dat$Zr)), parse = TRUE, hjust = "left",
size = 3.5) + labs(x = "", y = "", size = expression(paste(italic(n), " (# of species)")),
tag = "a") + theme_bw() + theme(legend.position = c(0, 1), legend.justification = c(0,
1)) + theme(legend.direction = "horizontal") + # theme(legend.background = element_rect(fill = 'white', colour = 'black'))
# +
theme(legend.background = element_blank()) + theme(axis.text.y = element_text(size = 10,
colour = "black", hjust = 0.5, angle = 90)) + annotation_custom(rasterGrob(image_mutualism),
xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) + annotation_custom(rasterGrob(image_parasitism),
xmin = -0.9, xmax = -0.7, ymin = 0.6, ymax = 1.2)
Figure 3a: A forest plot showing the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line) with observed effect sizes based on various sample sizes.
We ran a univariate meta-regression model for each of following moderators: 1) symbiosis, 2) host_tax_broad, 3) symbiont_tax_broad, 4) host_range_link_ratio, 5) host_range_taxonomic_breadth, 6) mode_of_transmission_broad, and 7) endo_or_ecto. The results from these models are presented in the main text.
In addition to these, we ran three more univariate models: 1) host_tax_symbiosis (equivalent to the interaction term between symbiosis and host_tax_symbiosis; symbiosis*host_tax_symbiosis), 2) symbiont_tax_symbiosis (symbiosis*symbiont_tax_broad), 3) host_symbiont_tax (host_tax_symbiosis*symbiont_tax_broad) and 4) symbiosis_transmission (symbiosis*mode_of_transmission_broad). There moderators are created below:
dat %<>%
# host_tax_broad*symbiosis (host_tax_symbiosis)
mutate(host_tax_symbiosis = str_c(host_tax_broad, symbiosis),
host_tax_symbiosis = ifelse(host_tax_symbiosis == "InvertNA", NA, host_tax_symbiosis),
host_tax_symbiosis = factor(host_tax_symbiosis),
# symbiont_tax_broad*symbiosis (symbiont_tax_symbiosis)
symbiont_tax_symbiosis = factor(str_c(symbiont_tax_broad, symbiosis)),
# host_tax_broad*symbiont_tax_broad (host_symbiont_tax)
host_symbiont_tax = factor(str_c(host_tax_broad, symbiont_tax_broad)),
# symbiosis*mode_of_transmission_broad (symbiosis_transmission)
symbiosis_transmission = factor(str_c(symbiosis, mode_of_transmission_broad)),
# whether p values were the smallest value given the number of randamization - limit_researched (Yes = 1, No = 0)
limit_rearched = if_else(abs((1/p_value) - no_randomizations) <= 1, 1, 0))
We first conducted a series of meta-regression model with one predictor.
# meta-regression: mutiple intercepts
mr_symbiosis1 <- rma.mv(yi = Zr, V = VZr, mods = ~symbiosis - 1, test = "t",
random = ~1 | authors, data = dat)
# meta-regression: contrast
mr_symbiosis2 <- rma.mv(yi = Zr, V = VZr, mods = ~symbiosis, test = "t", random = ~1 |
authors, data = dat)
Supplementary Table 2: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal]9 (R2) from the meta-regression with symbiosis.
# getting marginal R2
r2_symbiosis1 <- R2(mr_symbiosis1)
# getting estimates
res_symbiosis1 <- get_est(mr_symbiosis1, mod = "symbiosis")
res_symbiosis2 <- get_est(mr_symbiosis2, mod = "symbiosis")
# creating a table
tibble(`Fixed effect` = c(as.character(res_symbiosis1$name), cont_gen(res_symbiosis1$name)),
Estimate = c(res_symbiosis1$estimate, res_symbiosis2$estimate[2]), `Lower CI [0.025]` = c(res_symbiosis1$lowerCL,
res_symbiosis2$lowerCL[2]), `Upper CI [0.975]` = c(res_symbiosis1$upperCL,
res_symbiosis2$upperCL[2]), `V[authors]` = c(mr_symbiosis1$sigma2, rep(NA,
2)), R2 = c(r2_symbiosis1[1], rep(NA, 2))) %>% kable("html", digits = 3) %>%
kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Mutualist | 0.411 | 0.349 | 0.473 | 0.031 | 0.064 |
| Parasite | 0.311 | 0.268 | 0.354 | NA | NA |
| Mutualist-Parasite | -0.100 | -0.174 | -0.026 | NA | NA |
# adding sample size (k) for each category
k_symbiosis <- dat %>% group_by(symbiosis) %>% count()
# getting estimates and predicitons
pred_symbiosis <- get_pred(mr_symbiosis1, mod = "symbiosis")
res_symbiosis1 <- left_join(res_symbiosis1, k_symbiosis, by = c("name" = "symbiosis")) %>% left_join(pred_symbiosis)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_symbiosis <- ggplot(data = res_symbiosis1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(symbiosis)),
aes(x= tanh(Zr), y = symbiosis, size = ((1/VZr) + 3), colour = symbiosis), groupOnX = FALSE, alpha=0.2) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("Mutualist" = "#E69F00", "Parasite" = "#56B4E9")) +
scale_fill_manual(values = c("Mutualist" = "#E69F00", "Parasite" = "#56B4E9")) +
annotate('text', x = 0.93, y = c(1.15, 2.15), label= paste("italic(k)==", res_symbiosis1$n), parse = TRUE, hjust = "left", size = 3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0,1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_mutualism), xmin = -1, xmax = -0.8, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -1, xmax = -0.8, ymin = 1.6, ymax = 2.2)
fig_symbiosis
# fig 3
b <- ggplot(data = res_symbiosis1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(symbiosis)),
aes(x= tanh(Zr), y = symbiosis, size = ((1/VZr) + 3), colour = symbiosis), groupOnX = FALSE, alpha=0.2) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("Mutualist" = "#E69F00", "Parasite" = "#56B4E9")) +
scale_fill_manual(values = c("Mutualist" = "#E69F00", "Parasite" = "#56B4E9")) +
annotate('text', x = 0.93, y = c(1.15, 2.15), label= paste("italic(k)==", res_symbiosis1$n), parse = TRUE, hjust = "left", size = 3.5) +
labs(x = "", y = "", tag = "b") +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position="none") +
theme(axis.text.y = element_text(size = 10, colour ="black",hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_mutualism), xmin = -1, xmax = -0.8, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -1, xmax = -0.8, ymin = 1.6, ymax = 2.2)
Figure 3b: A forest plot showing the group-wise means (the categorical variable symbiosis) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
# reordering
dat$host_tax_broad <- factor(dat$host_tax_broad, levels = c("Microbe", "Plant",
"Invert", "Vert"))
# meta-regression: mutiple intercepts
mr_host_tax_broad1 <- rma.mv(yi = Zr, V = VZr, mods = ~host_tax_broad - 1, test = "t",
random = ~1 | authors, data = dat)
# meta-regression: contrast 1
mr_host_tax_broad2 <- rma.mv(yi = Zr, V = VZr, mods = ~host_tax_broad, test = "t",
random = ~1 | authors, data = dat)
# meta-regression: contrast 2
mr_host_tax_broad3 <- rma.mv(yi = Zr, V = VZr, mods = ~relevel(host_tax_broad,
ref = "Plant"), test = "t", random = ~1 | authors, data = dat)
# meta-regression: contrast 3
mr_host_tax_broad4 <- rma.mv(yi = Zr, V = VZr, mods = ~relevel(host_tax_broad,
ref = "Invert"), test = "t", random = ~1 | authors, data = dat)
Supplementary Table 3: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with host_tax_broad.
# getting marginal R2
r2_host_tax_broad1 <- R2(mr_host_tax_broad1)
# getting estimates
res_host_tax_broad1 <- get_est(mr_host_tax_broad1, mod = "host_tax_broad")
res_host_tax_broad2 <- get_est(mr_host_tax_broad2, mod = "host_tax_broad")
# the name bit does not work if relevel....
res_host_tax_broad3 <- get_est(mr_host_tax_broad3, mod = "host_tax_broad")
res_host_tax_broad4 <- get_est(mr_host_tax_broad4, mod = "host_tax_broad")
# creating a table
tibble(`Fixed effect` = c(as.character(res_host_tax_broad1$name), cont_gen(res_host_tax_broad1$name)),
Estimate = c(res_host_tax_broad1$estimate, res_host_tax_broad2$estimate[-1],
res_host_tax_broad3$estimate[-(1:2)], res_host_tax_broad4$estimate[-(1:3)]),
`Lower CI [0.025]` = c(res_host_tax_broad1$lowerCL, res_host_tax_broad2$lowerCL[-1],
res_host_tax_broad3$lowerCL[-(1:2)], res_host_tax_broad4$lowerCL[-(1:3)]),
`Upper CI [0.975]` = c(res_host_tax_broad1$upperCL, res_host_tax_broad2$upperCL[-1],
res_host_tax_broad3$upperCL[-(1:2)], res_host_tax_broad4$upperCL[-(1:3)]),
`V[authors]` = c(mr_host_tax_broad1$sigma2, rep(NA, 9)), R2 = c(r2_host_tax_broad1[1],
rep(NA, 9))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left") %>% scroll_box(width = "100%", height = "300px")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Microbe | 0.557 | 0.404 | 0.710 | 0.03 | 0.168 |
| Plant | 0.244 | 0.168 | 0.321 | NA | NA |
| Invert | 0.412 | 0.344 | 0.480 | NA | NA |
| Vert | 0.327 | 0.277 | 0.378 | NA | NA |
| Microbe-Plant | -0.312 | -0.483 | -0.141 | NA | NA |
| Microbe-Invert | -0.144 | -0.312 | 0.023 | NA | NA |
| Microbe-Vert | -0.229 | -0.390 | -0.068 | NA | NA |
| Plant-Invert | 0.168 | 0.065 | 0.270 | NA | NA |
| Plant-Vert | 0.083 | -0.009 | 0.175 | NA | NA |
| Invert-Vert | -0.085 | -0.168 | -0.002 | NA | NA |
# getting images
image_invertebrate_host <- readPNG("../images/invertebrate_host_transparentbg.png")
image_microbe_host <- readPNG("../images/microbe_host_transparentbg.png")
image_vertebrate_host <- readPNG("../images/vertebrate_host_transparentbg.png")
image_plant_host <- readPNG("../images/plant_host_transparentbg.png")
# adding sample size (k) for each category
k_host_tax_broad <- dat %>% group_by(host_tax_broad) %>% count()
# getting estimates and predicitons
pred_host_tax_broad <- get_pred(mr_host_tax_broad1, mod = "host_tax_broad")
res_host_tax_broad1 <- left_join(res_host_tax_broad1, k_host_tax_broad, by = c("name" = "host_tax_broad")) %>% left_join(pred_host_tax_broad)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_host_tax_broad <- ggplot(data = res_host_tax_broad1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(host_tax_broad)),
aes(x= tanh(Zr), y = host_tax_broad, size = ((1/VZr) + 3), colour = host_tax_broad), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("Microbe" = "#009E73", "Plant" = "#F0E422", "Invert"= "#0072B2", "Vert" = "#D55E00")) +
scale_fill_manual(values = c("Microbe" = "#009E73", "Plant" = "#F0E422", "Invert"= "#0072B2", "Vert" = "#D55E00")) +
scale_y_discrete(labels = c("Microbe" = "Microbe", "Plant" = "Plant", "Invert"= "Invertebrate", "Vert" = "Vertebrate")) +
annotate('text', x = 0.93, y = 1:4 + 0.15, label= paste("italic(k)==", res_host_tax_broad1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0,1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_microbe_host), xmin = -1, xmax = -0.8, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_plant_host), xmin = -1, xmax = -0.8, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_invertebrate_host), xmin = -1, xmax = -0.8, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_vertebrate_host), xmin = -1, xmax = -0.8, ymin = 3.6, ymax = 4.2)
fig_host_tax_broad
# fig 3c
c <- ggplot(data = res_host_tax_broad1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(host_tax_broad)),
aes(x= tanh(Zr), y = host_tax_broad, size = ((1/VZr) + 3), colour = host_tax_broad), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("Microbe" = "#009E73", "Plant" = "#F0E422", "Invert"= "#0072B2", "Vert" = "#D55E00")) +
scale_fill_manual(values = c("Microbe" = "#009E73", "Plant" = "#F0E422", "Invert"= "#0072B2", "Vert" = "#D55E00")) +
scale_y_discrete(labels = c("Microbe" = "Microbe", "Plant" = "Plant", "Invert"= "Invertebrate", "Vert" = "Vertebrate")) +
annotate('text', x = 0.93, y = 1:4 + 0.15, label= paste("italic(k)==", res_host_tax_broad1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = "", y = "", size = expression(paste(italic(n), " (# of species)")) , tag = "c") +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position="none") +
theme(axis.text.y = element_text(size = 10, colour ="black",hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_microbe_host), xmin = -1, xmax = -0.8, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_plant_host), xmin = -1, xmax = -0.8, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_invertebrate_host), xmin = -1, xmax = -0.8, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_vertebrate_host), xmin = -1, xmax = -0.8, ymin = 3.6, ymax = 4.2)
Figure 3c: A forest plot showing the group-wise means (the categorical variable host_tax_broad) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
# reordering
dat$symbiont_tax_broad <- factor(dat$symbiont_tax_broad, levels = c("Microbe",
"Plant", "Invert", "Vert"))
# sizes <- factor(sizes, levels = c('small', 'medium', 'large')) sizes > [1]
# small large large small medium > Levels: small medium large
# meta-regression: mutiple intercepts
mr_symbiont_tax_broad1 <- rma.mv(yi = Zr, V = VZr, mods = ~symbiont_tax_broad -
1, test = "t", random = ~1 | authors, data = dat)
# meta-regression: contrast 1
mr_symbiont_tax_broad2 <- rma.mv(yi = Zr, V = VZr, mods = ~symbiont_tax_broad,
test = "t", random = ~1 | authors, data = dat)
# meta-regression: contrast 2
mr_symbiont_tax_broad3 <- rma.mv(yi = Zr, V = VZr, mods = ~relevel(symbiont_tax_broad,
ref = "Plant"), test = "t", random = ~1 | authors, data = dat)
# meta-regression: contrast 3
mr_symbiont_tax_broad4 <- rma.mv(yi = Zr, V = VZr, mods = ~relevel(symbiont_tax_broad,
ref = "Invert"), test = "t", random = ~1 | authors, data = dat)
Supplementary Table 4: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# getting marginal R2
r2_symbiont_tax_broad1 <- R2(mr_symbiont_tax_broad1)
# getting estimates
res_symbiont_tax_broad1 <- get_est(mr_symbiont_tax_broad1, mod = "symbiont_tax_broad")
res_symbiont_tax_broad2 <- get_est(mr_symbiont_tax_broad2, mod = "symbiont_tax_broad")
res_symbiont_tax_broad3 <- get_est(mr_symbiont_tax_broad3, mod = "symbiont_tax_broad")
res_symbiont_tax_broad4 <- get_est(mr_symbiont_tax_broad4, mod = "symbiont_tax_broad")
# creating a table
tibble(`Fixed effect` = c(as.character(res_symbiont_tax_broad1$name), cont_gen(res_symbiont_tax_broad1$name)),
Estimate = c(res_symbiont_tax_broad1$estimate, res_symbiont_tax_broad2$estimate[-1],
res_symbiont_tax_broad3$estimate[-(1:2)], res_symbiont_tax_broad4$estimate[-(1:3)]),
`Lower CI [0.025]` = c(res_symbiont_tax_broad1$lowerCL, res_symbiont_tax_broad2$lowerCL[-1],
res_symbiont_tax_broad3$lowerCL[-(1:2)], res_symbiont_tax_broad4$lowerCL[-(1:3)]),
`Upper CI [0.975]` = c(res_symbiont_tax_broad1$upperCL, res_symbiont_tax_broad2$upperCL[-1],
res_symbiont_tax_broad3$upperCL[-(1:2)], res_symbiont_tax_broad4$upperCL[-(1:3)]),
`V[authors]` = c(mr_symbiont_tax_broad1$sigma2, rep(NA, 9)), R2 = c(r2_symbiont_tax_broad1[1],
rep(NA, 9))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left") %>% scroll_box(width = "100%", height = "300px")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Microbe | 0.353 | 0.303 | 0.403 | 0.032 | 0.082 |
| Plant | 0.733 | 0.440 | 1.027 | NA | NA |
| Invert | 0.324 | 0.270 | 0.377 | NA | NA |
| Vert | 0.291 | -0.112 | 0.694 | NA | NA |
| Microbe-Plant | 0.380 | 0.083 | 0.678 | NA | NA |
| Microbe-Invert | -0.029 | -0.102 | 0.044 | NA | NA |
| Microbe-Vert | -0.062 | -0.468 | 0.345 | NA | NA |
| Plant-Invert | -0.410 | -0.708 | -0.111 | NA | NA |
| Plant-Vert | -0.442 | -0.941 | 0.057 | NA | NA |
| Invert-Vert | -0.033 | -0.440 | 0.374 | NA | NA |
# getting images
image_invertebrate_parasite <- readPNG("../images/invertebrate_parasite_transparentbg.png")
image_microbe_parasite <- readPNG("../images/microbe_parasite_transparentbg.png")
image_vertebrate_parasite <- readPNG("../images/vertebrate_parasite_transparentbg.png")
image_plant_parasite <- readPNG("../images/plant_parasite_transparentbg.png")
# adding sample size (k) for each category
k_symbiont_tax_broad <- dat %>% group_by(symbiont_tax_broad) %>% count()
# getting estimates and predicitons
pred_symbiont_tax_broad <- get_pred(mr_symbiont_tax_broad1, mod = "symbiont_tax_broad")
res_symbiont_tax_broad1 <- left_join(res_symbiont_tax_broad1, k_symbiont_tax_broad, by = c("name" = "symbiont_tax_broad")) %>% left_join(pred_symbiont_tax_broad)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_symbiont_tax_broad <- ggplot(data = res_symbiont_tax_broad1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(symbiont_tax_broad)),
aes(x= tanh(Zr), y = symbiont_tax_broad, size = ((1/VZr) + 3), colour = symbiont_tax_broad), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("Microbe" = "#009E73", "Plant" = "#F0E422", "Invert"= "#0072B2", "Vert" = "#D55E00" )) +
scale_fill_manual(values = c("Microbe" = "#009E73", "Plant" = "#F0E422", "Invert"= "#0072B2", "Vert" = "#D55E00")) +
scale_y_discrete(labels = c("Microbe" = "Microbe", "Plant" = "Plant", "Invert"= "Invertebrate", "Vert" = "Vertebrate")) +
annotate('text', x = 0.93, y = 1:4 + 0.15, label= paste("italic(k)==", res_symbiont_tax_broad1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0,1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_microbe_parasite), xmin = -1, xmax = -0.8, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_plant_parasite), xmin = -1, xmax = -0.8, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_invertebrate_parasite), xmin = -1, xmax = -0.8, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_vertebrate_parasite), xmin = -1, xmax = -0.8, ymin = 3.6, ymax = 4.2)
fig_symbiont_tax_broad
# fig 3d
d <- ggplot(data = res_symbiont_tax_broad1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(symbiont_tax_broad)),
aes(x= tanh(Zr), y = symbiont_tax_broad, size = ((1/VZr) + 3), colour = symbiont_tax_broad), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("Microbe" = "#009E73", "Plant" = "#F0E422", "Invert"= "#0072B2", "Vert" = "#D55E00" )) +
scale_fill_manual(values = c("Microbe" = "#009E73", "Plant" = "#F0E422", "Invert"= "#0072B2", "Vert" = "#D55E00")) +
scale_y_discrete(labels = c("Microbe" = "Microbe", "Plant" = "Plant", "Invert"= "Invertebrate", "Vert" = "Vertebrate")) +
annotate('text', x = 0.93, y = 1:4 + 0.15, label= paste("italic(k)==", res_symbiont_tax_broad1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")), tag = "d") +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position="none") +
theme(axis.text.y = element_text(size = 10, colour ="black",hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_microbe_parasite), xmin = -1, xmax = -0.8, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_plant_parasite), xmin = -1, xmax = -0.8, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_invertebrate_parasite), xmin = -1, xmax = -0.8, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_vertebrate_parasite), xmin = -1, xmax = -0.8, ymin = 3.6, ymax = 4.2)
Figure 2d: A forest plot showing the group-wise means (the categorical variable symbiont_tax_broad) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
# meta-regression
mr_host_range_link_ratio <- rma.mv(yi = Zr, V = VZr, mods = ~log(host_range_link_ratio),
random = ~1 | authors, data = dat)
Supplementary Table 5: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with log(host_range_link_ratio).
# getting marginal R2
r2_host_range_link_ratio <- R2(mr_host_range_link_ratio)
# getting estimates: name does not work for slopes
res_host_range_link_ratio <- get_est(mr_host_range_link_ratio, mod = "log(host_range_link_ratio)")
# creating a table
tibble(`Fixed effect` = c("Intercept", "log(host_range_link_ratio)"), Estimate = c(res_host_range_link_ratio$estimate),
`Lower CI [0.025]` = c(res_host_range_link_ratio$lowerCL), `Upper CI [0.975]` = c(res_host_range_link_ratio$upperCL),
`V[authors]` = c(mr_host_range_link_ratio$sigma2, NA), R2 = c(r2_host_range_link_ratio[1],
NA)) %>% kable("html", digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Intercept | 0.360 | 0.318 | 0.401 | 0.033 | 0.012 |
| log(host_range_link_ratio) | -0.057 | -0.150 | 0.037 | NA | NA |
# newmods <- seq(-0.3, 2.2, by = 0.1)
# pred_host_range_link_ratio <-predict.rma(mr_host_range_link_ratio, newmods = newmods)
# ribbon_dat <- tibble(newmods = newmods, ymin = pred_host_range_link_ratio$ci.lb, ymax = pred_host_range_link_ratio$ci.ub)
pred_host_range_link_ratio <-predict.rma(mr_host_range_link_ratio)
# plotting
fig_host_range_link_ratio <- dat %>%
filter(!is.na(host_range_link_ratio)) %>% # getting ride of NA values
mutate(ymin = pred_host_range_link_ratio$ci.lb,
ymax = pred_host_range_link_ratio$ci.ub,
ymin2 = pred_host_range_link_ratio$cr.lb,
ymax2 = pred_host_range_link_ratio$cr.ub,
pred = pred_host_range_link_ratio$pred) %>%
ggplot(aes(x = log(host_range_link_ratio), y = Zr, size = (1/VZr) + 3, )) +
geom_point(shape = 21, fill = "grey90") +
#geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2") + # not quite sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymin), method = "loess", se = FALSE,lty = "dotted", lwd = 0.25, colour ="#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty ="dotted", lwd = 0.25, colour ="#D55E00") +
geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty ="dashed", lwd = 0.5, colour ="black") +
ylim(-1, 2) + xlim(-0.05, 2) +
#geom_abline(intercept = mr_host_range_link_ratio$beta[[1]], slope = mr_host_range_link_ratio$beta[[2]], alpha = 0.7, linetype = "dashed", size = 0.5) +
labs(x = "ln(host range link ratio)", y = expression(paste(italic(Zr), " (effect size)")), size = expression(paste(italic(n), " (# of species)"))) +
guides(fill = "none", colour = "none") +
# themses
theme_bw() +
theme(legend.position= c(1, 1), legend.justification = c(1, 1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90))
fig_host_range_link_ratio
Supplementary Figure 1: A bubble plot showing a predicted regression line for the contentious variable log(host_range_link_ratio) with their 95% confidences regions (orange dotted lines) and 95% prediction regions (blue dotted lines) with observed effect sizes based on various sample sizes.
# meta-regression
mr_host_range_taxonomic_breadth <- rma.mv(yi = Zr, V = VZr, mods = ~log(host_range_taxonomic_breadth),
random = ~1 | authors, data = dat)
Supplementary Table 6: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with log(host_range_taxonomic_breadth).
# getting marginal R2
r2_host_range_taxonomic_breadth <- R2(mr_host_range_taxonomic_breadth)
# getting estimates: name does not work for slopes
res_host_range_taxonomic_breadth <- get_est(mr_host_range_taxonomic_breadth,
mod = "log(host_range_taxonomic_breadth)")
# creating a table
tibble(`Fixed effect` = c("Intercept", "log(host_range_taxonomic_breadth)"),
Estimate = c(res_host_range_taxonomic_breadth$estimate), `Lower CI [0.025]` = c(res_host_range_taxonomic_breadth$lowerCL),
`Upper CI [0.975]` = c(res_host_range_taxonomic_breadth$upperCL), `V[authors]` = c(mr_host_range_taxonomic_breadth$sigma2,
NA), R2 = c(r2_host_range_taxonomic_breadth[1], NA)) %>% kable("html",
digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Intercept | 0.364 | 0.323 | 0.405 | 0.032 | 0.008 |
| log(host_range_taxonomic_breadth) | -0.056 | -0.170 | 0.057 | NA | NA |
pred_host_range_taxonomic_breadth <-predict.rma(mr_host_range_taxonomic_breadth)
# plotting
fig_host_range_taxonomic_breadth <- dat %>%
filter(!is.na(host_range_taxonomic_breadth)) %>% # getting ride of NA values
mutate(ymin = pred_host_range_taxonomic_breadth$ci.lb,
ymax = pred_host_range_taxonomic_breadth$ci.ub,
ymin2 = pred_host_range_taxonomic_breadth$cr.lb,
ymax2 = pred_host_range_taxonomic_breadth$cr.ub,
pred = pred_host_range_taxonomic_breadth$pred) %>%
ggplot(aes(x = log(host_range_taxonomic_breadth), y = Zr, size = (1/VZr) + 3, )) +
geom_point(shape = 21, fill = "grey90") +
#geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2") + # not quite sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymin), method = "loess", se = FALSE,lty = "dotted", lwd = 0.25, colour ="#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty ="dotted", lwd = 0.25, colour ="#D55E00") +
geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty ="dashed", lwd = 0.5, colour ="black") +
ylim(-1, 2) + xlim(0, 1.5) +
#geom_abline(intercept = mr_host_range_link_ratio$beta[[1]], slope = mr_host_range_link_ratio$beta[[2]], alpha = 0.7, linetype = "dashed", size = 0.5) +
labs(x = "ln(host range taxonomic breadth)", y = expression(paste(italic(Zr), " (effect size)")), size = expression(paste(italic(n), " (# of species)"))) +
guides(fill = "none", colour = "none") +
# themses
theme_bw() +
theme(legend.position= c(1, 1), legend.justification = c(1, 1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90))
fig_host_range_taxonomic_breadth
Supplementary Figure 2: A bubble plot showing a predicted regression line for the contentious variable log(log(host_range_taxonomic_breadth) with their 95% confidences regions (orange dotted lines) and 95% prediction regions (blue dotted lines) with observed effect sizes based on various sample sizes.
# reordering
dat$endo_or_ecto <- factor(dat$endo_or_ecto, levels = c("Endo/Ecto", "Endo",
"Ecto"))
# meta-regression: mutiple intercepts
mr_endo_or_ecto1 <- rma.mv(yi = Zr, V = VZr, mods = ~endo_or_ecto - 1, test = "t",
random = ~1 | authors, data = dat)
# meta-regression: contrast 1
mr_endo_or_ecto2 <- rma.mv(yi = Zr, V = VZr, mods = ~endo_or_ecto, test = "t",
random = ~1 | authors, data = dat)
# meta-regression: contrast 2
mr_endo_or_ecto3 <- rma.mv(yi = Zr, V = VZr, mods = ~relevel(endo_or_ecto, ref = "Endo"),
test = "t", random = ~1 | authors, data = dat)
Supplementary Table 7: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with endo_or_ecto.
# getting marginal R2
r2_endo_or_ecto1 <- R2(mr_endo_or_ecto1)
# getting estimates
res_endo_or_ecto1 <- get_est(mr_endo_or_ecto1, mod = "endo_or_ecto")
res_endo_or_ecto2 <- get_est(mr_endo_or_ecto2, mod = "endo_or_ecto")
res_endo_or_ecto3 <- get_est(mr_endo_or_ecto3, mod = "endo_or_ecto")
# creating a table
tibble(`Fixed effect` = c(as.character(res_endo_or_ecto1$name), cont_gen(res_endo_or_ecto1$name)),
Estimate = c(res_endo_or_ecto1$estimate, res_endo_or_ecto2$estimate[-1],
res_endo_or_ecto3$estimate[-(1:2)]), `Lower CI [0.025]` = c(res_endo_or_ecto1$lowerCL,
res_endo_or_ecto2$lowerCL[-1], res_endo_or_ecto3$lowerCL[-(1:2)]), `Upper CI [0.975]` = c(res_endo_or_ecto1$upperCL,
res_endo_or_ecto2$upperCL[-1], res_endo_or_ecto3$upperCL[-(1:2)]), `V[authors]` = c(mr_endo_or_ecto1$sigma2,
rep(NA, 5)), R2 = c(r2_endo_or_ecto1[1], rep(NA, 5))) %>% kable("html",
digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Endo/Ecto | 0.334 | 0.035 | 0.632 | 0.034 | 0.001 |
| Endo | 0.342 | 0.298 | 0.387 | NA | NA |
| Ecto | 0.353 | 0.287 | 0.418 | NA | NA |
| Endo/Ecto-Endo | 0.009 | -0.293 | 0.311 | NA | NA |
| Endo/Ecto-Ecto | 0.019 | -0.287 | 0.325 | NA | NA |
| Endo-Ecto | 0.010 | -0.069 | 0.090 | NA | NA |
# getting images
image_endoparasite <- readPNG("../images/endoparasite_transparentbg.png")
image_ectoparasite <- readPNG("../images/ectoparasite_transparentbg.png")
# adding sample size (k) for each category
k_endo_or_ecto <- dat %>% group_by(endo_or_ecto) %>% count()
# getting estimates and predicitons
pred_endo_or_ecto <- get_pred(mr_endo_or_ecto1, mod = "endo_or_ecto")
res_endo_or_ecto1 <- left_join(res_endo_or_ecto1, k_endo_or_ecto, by = c("name" = "endo_or_ecto")) %>% left_join(pred_endo_or_ecto)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_endo_or_ecto <- ggplot(data = res_endo_or_ecto1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(endo_or_ecto)),
aes(x= tanh(Zr), y = endo_or_ecto, size = ((1/VZr) + 3), colour = endo_or_ecto), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("Endo/Ecto" = "#0072B2", "Endo" = "#D55E00", "Ecto"= "#CC79A7")) +
scale_fill_manual(values = c("Endo/Ecto" = "#0072B2", "Endo" = "#D55E00", "Ecto"= "#CC79A7")) +
scale_y_discrete(labels = c("Endo/Ecto" = "Both", "Endo" = "Endosymbiosis", "Ecto"= "Ectosymbiosis")) +
annotate('text', x = 0.93, y = 1:3 + 0.15, label= paste("italic(k)==", res_endo_or_ecto1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0,1)) +
theme(legend.direction = "horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# adding images
annotation_custom(rasterGrob(image_endoparasite), xmin = -1, xmax = -0.8, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_ectoparasite), xmin = -1, xmax = -0.8, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_ectoparasite), xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_endoparasite), xmin = -0.9, xmax = -0.7, ymin = 0.6, ymax = 1.2)
fig_endo_or_ecto
# fig 3e
e <- ggplot(data = res_endo_or_ecto1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(endo_or_ecto)),
aes(x= tanh(Zr), y = endo_or_ecto, size = ((1/VZr) + 3), colour = endo_or_ecto), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("Endo/Ecto" = "#0072B2", "Endo" = "#D55E00", "Ecto"= "#CC79A7")) +
scale_fill_manual(values = c("Endo/Ecto" = "#0072B2", "Endo" = "#D55E00", "Ecto"= "#CC79A7")) +
scale_y_discrete(labels = c("Endo/Ecto" = "Both", "Endo" = "Endosymbiosis", "Ecto"= "Ectosymbiosis")) +
annotate('text', x = 0.93, y = 1:3 + 0.15, label= paste("italic(k)==", res_endo_or_ecto1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = "", y = "", size = expression(paste(italic(n), " (# of species)")), tag = "e" ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position="none") +
theme(axis.text.y = element_text(size = 10, colour ="black",hjust = 0.5, angle = 90)) +
# adding images
annotation_custom(rasterGrob(image_endoparasite), xmin = -1, xmax = -0.8, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_ectoparasite), xmin = -1, xmax = -0.8, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_ectoparasite), xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_endoparasite), xmin = -0.9, xmax = -0.7, ymin = 0.6, ymax = 1.2)
Figure 3e: A forest plot showing the group-wise means (the categorical variable endo_or_ecto) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
# meta-regression: mutiple intercepts
mr_mode_of_transmission_broad1 <- rma.mv(yi = Zr, V = VZr, mods = ~mode_of_transmission_broad -
1, test = "t", random = ~1 | authors, data = dat)
# meta-regression: contrast 1
mr_mode_of_transmission_broad2 <- rma.mv(yi = Zr, V = VZr, mods = ~mode_of_transmission_broad,
test = "t", random = ~1 | authors, data = dat)
# meta-regression: contrast 2
mr_mode_of_transmission_broad3 <- rma.mv(yi = Zr, V = VZr, mods = ~relevel(mode_of_transmission_broad,
ref = "vertical"), test = "t", random = ~1 | authors, data = dat)
Supplementary Table 8: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with mode_of_transmission_broad.
# getting marginal R2
r2_mode_of_transmission_broad1 <- R2(mr_mode_of_transmission_broad1)
# getting estimates
res_mode_of_transmission_broad1 <- get_est(mr_mode_of_transmission_broad1, mod = "mode_of_transmission_broad")
res_mode_of_transmission_broad2 <- get_est(mr_mode_of_transmission_broad2, mod = "mode_of_transmission_broad")
res_mode_of_transmission_broad3 <- get_est(mr_mode_of_transmission_broad3, mod = "mode_of_transmission_broad")
# creating a table
tibble(`Fixed effect` = c(as.character(res_mode_of_transmission_broad1$name),
cont_gen(res_mode_of_transmission_broad1$name)), Estimate = c(res_mode_of_transmission_broad1$estimate,
res_mode_of_transmission_broad2$estimate[-1], res_mode_of_transmission_broad3$estimate[-(1:2)]),
`Lower CI [0.025]` = c(res_mode_of_transmission_broad1$lowerCL, res_mode_of_transmission_broad2$lowerCL[-1],
res_mode_of_transmission_broad3$lowerCL[-(1:2)]), `Upper CI [0.975]` = c(res_mode_of_transmission_broad1$upperCL,
res_mode_of_transmission_broad2$upperCL[-1], res_mode_of_transmission_broad3$upperCL[-(1:2)]),
`V[authors]` = c(mr_mode_of_transmission_broad1$sigma2, rep(NA, 5)), R2 = c(r2_mode_of_transmission_broad1[1],
rep(NA, 5))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| both | 0.371 | 0.298 | 0.443 | 0.024 | 0.219 |
| horizontal | 0.285 | 0.242 | 0.328 | NA | NA |
| vertical | 0.496 | 0.421 | 0.570 | NA | NA |
| both-horizontal | -0.085 | -0.170 | -0.001 | NA | NA |
| both-vertical | 0.125 | 0.021 | 0.229 | NA | NA |
| horizontal-vertical | -0.210 | -0.297 | -0.124 | NA | NA |
# getting images
image_horizontal <- readPNG("../images/horizontal_transparentbg.png")
image_vertical <- readPNG("../images/vertical_transparentbg.png")
image_both <- readPNG("../images/horizontal_vertical_transparentbg.png")
# adding sample size (k) for each category
k_mode_of_transmission_broad <- dat %>% group_by(mode_of_transmission_broad) %>% count()
# getting estimates and predicitons
pred_mode_of_transmission_broad <- get_pred(mr_mode_of_transmission_broad1, mod = "mode_of_transmission_broad")
res_mode_of_transmission_broad1 <- left_join(res_mode_of_transmission_broad1, k_mode_of_transmission_broad, by = c("name" = "mode_of_transmission_broad")) %>% left_join(pred_mode_of_transmission_broad)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_mode_of_transmission_broad <- ggplot(data = res_mode_of_transmission_broad1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(mode_of_transmission_broad)),
aes(x= tanh(Zr), y = mode_of_transmission_broad, size = ((1/VZr) + 3), colour = mode_of_transmission_broad), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("both" = "#0072B2", "horizontal" = "#D55E00", "vertical"= "#CC79A7")) +
scale_fill_manual(values = c("both" = "#0072B2", "horizontal" = "#D55E00", "vertical"= "#CC79A7")) +
scale_y_discrete(labels = c("both" = "Both", "horizontal" = "Horizontal", "vertical"= "Vertical")) +
annotate('text', x = 0.93, y = (1:3 + 0.15), label= paste("italic(k)==", res_mode_of_transmission_broad1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0,1)) +
theme(legend.direction = "horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# adding images
annotation_custom(rasterGrob(image_horizontal), xmin = -1, xmax = -0.7, ymin = 1.4, ymax = 2.2) +
annotation_custom(rasterGrob(image_vertical), xmin = -1, xmax = -0.7, ymin = 2.4, ymax = 3.2) +
annotation_custom(rasterGrob(image_both), xmin = -1, xmax = -0.7, ymin = 0.4, ymax = 1.2)
fig_mode_of_transmission_broad
# fig 3f
f <- ggplot(data = res_mode_of_transmission_broad1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(mode_of_transmission_broad)),
aes(x= tanh(Zr), y = mode_of_transmission_broad, size = ((1/VZr) + 3), colour = mode_of_transmission_broad), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("both" = "#0072B2", "horizontal" = "#D55E00", "vertical"= "#CC79A7")) +
scale_fill_manual(values = c("both" = "#0072B2", "horizontal" = "#D55E00", "vertical"= "#CC79A7")) +
scale_y_discrete(labels = c("both" = "Both", "horizontal" = "Horizontal", "vertical"= "Vertical")) +
annotate('text', x = 0.93, y = (1:3 + 0.15), label= paste("italic(k)==", res_mode_of_transmission_broad1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = "", y = "", size = expression(paste(italic(n), " (# of species)")), tag = "f" ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position="none") +
theme(axis.text.y = element_text(size = 10, colour ="black",hjust = 0.5, angle = 90)) +
# adding images
annotation_custom(rasterGrob(image_horizontal), xmin = -1, xmax = -0.7, ymin = 1.4, ymax = 2.2) +
annotation_custom(rasterGrob(image_vertical), xmin = -1, xmax = -0.7, ymin = 2.4, ymax = 3.2) +
annotation_custom(rasterGrob(image_both), xmin = -1, xmax = -0.7, ymin = 0.4, ymax = 1.2)
Figure 2f: A forest plot showing the group-wise means (the categorical variable mode_of_transmission_broad) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
# reordering
dat$symbiosis_transmission <- factor(dat$symbiosis_transmission, levels = c("Mutualistboth",
"Mutualisthorizontal", "Mutualistvertical", "Parasiteboth", "Parasitehorizontal"),
labels = c("MutualistBoth", "MutualistHorizontal", "MutualistVertical",
"ParasiteBoth", "ParasiteHorizontal"))
# meta-regression: mutiple intercepts
mr_symbiosis_transmission1 <- rma.mv(yi = Zr, V = VZr, mods = ~symbiosis_transmission -
1, test = "t", random = ~1 | authors, data = dat)
# # meta-regression: contrasts x 10 getting the level names out
level_names <- levels(dat$symbiosis_transmission)
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = Zr, V = VZr, mods = ~relevel(symbiosis_transmission, ref = name),
test = "t", random = ~1 | authors, data = dat)
}
# results of meta-regression including all contrast results; taking the last
# level out ([-length(level_names)])
mr_symbiosis_transmission <- map(level_names[-length(level_names)], run_rma)
Supplementary Table 9: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiosis_transmission.
# getting marginal R2
r2_symbiosis_transmission1 <- R2(mr_symbiosis_transmission1)
# getting estimates
res_symbiosis_transmission1 <- get_est(mr_symbiosis_transmission1, mod = "symbiosis_transmission")
res_symbiosis_transmission <- map(mr_symbiosis_transmission, ~get_est(.x, mod = "symbiosis_transmission"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from = 1, to = 1:4)
# you need to flatten twice: first to make it a list and make it a vector
estimates <- map2(res_symbiosis_transmission, contra_list, ~.x[-(.y), "estimate"]) %>%
flatten() %>% flatten_dbl()
lowerCLs <- map2(res_symbiosis_transmission, contra_list, ~.x[-(.y), "lowerCL"]) %>%
flatten() %>% flatten_dbl()
upperCLs <- map2(res_symbiosis_transmission, contra_list, ~.x[-(.y), "upperCL"]) %>%
flatten() %>% flatten_dbl()
# creating a table
tibble(`Fixed effect` = c(as.character(res_symbiosis_transmission1$name), cont_gen(res_symbiosis_transmission1$name)),
Estimate = c(res_symbiosis_transmission1$estimate, estimates), `Lower CI [0.025]` = c(res_symbiosis_transmission1$lowerCL,
lowerCLs), `Upper CI [0.975]` = c(res_symbiosis_transmission1$upperCL,
upperCLs), `V[authors]` = c(mr_symbiosis_transmission1$sigma2, rep(NA,
(5 + choose(5, 2)) - 1)), R2 = c(r2_symbiosis_transmission1[1], rep(NA,
(5 + choose(5, 2)) - 1))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left") %>% scroll_box(width = "100%", height = "300px")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| MutualistBoth | 0.453 | 0.224 | 0.682 | 0.024 | 0.213 |
| MutualistHorizontal | 0.290 | 0.189 | 0.391 | NA | NA |
| MutualistVertical | 0.493 | 0.417 | 0.568 | NA | NA |
| ParasiteBoth | 0.362 | 0.285 | 0.439 | NA | NA |
| ParasiteHorizontal | 0.284 | 0.238 | 0.331 | NA | NA |
| MutualistBoth-MutualistHorizontal | -0.163 | -0.413 | 0.088 | NA | NA |
| MutualistBoth-MutualistVertical | 0.040 | -0.201 | 0.281 | NA | NA |
| MutualistBoth-ParasiteBoth | -0.091 | -0.333 | 0.150 | NA | NA |
| MutualistBoth-ParasiteHorizontal | -0.168 | -0.402 | 0.065 | NA | NA |
| MutualistHorizontal-MutualistVertical | 0.202 | 0.076 | 0.329 | NA | NA |
| MutualistHorizontal-ParasiteBoth | 0.071 | -0.056 | 0.198 | NA | NA |
| MutualistHorizontal-ParasiteHorizontal | -0.006 | -0.115 | 0.103 | NA | NA |
| MutualistVertical-ParasiteBoth | -0.131 | -0.239 | -0.023 | NA | NA |
| MutualistVertical-ParasiteHorizontal | -0.208 | -0.297 | -0.119 | NA | NA |
| ParasiteBoth-ParasiteHorizontal | -0.077 | -0.167 | 0.013 | NA | NA |
# colour list
colour_ls <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E422", "#0072B2", "#D55E00", "#CC79A7", "#00008B", "#8B0A50", "#54FF9F", "#999999")
# adding sample size (k) for each category
k_symbiosis_transmission <- dat %>% group_by(symbiosis_transmission) %>% count()
# getting estimates and predicitons
pred_symbiosis_transmission <- get_pred(mr_symbiosis_transmission1, mod = "symbiosis_transmission")
res_symbiosis_transmission1 <- left_join(res_symbiosis_transmission1, k_symbiosis_transmission, by = c("name" = "symbiosis_transmission")) %>% left_join(pred_symbiosis_transmission)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_symbiosis_transmission <- ggplot(data = res_symbiosis_transmission1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(symbiosis_transmission)),
aes(x= tanh(Zr), y = symbiosis_transmission, size = ((1/VZr) + 3), colour = symbiosis_transmission), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("MutualistBoth"= colour_ls[1], "MutualistHorizontal"= colour_ls[2], "MutualistVertical" = colour_ls[3],"ParasiteBoth"= colour_ls[4], "ParasiteHorizontal" = colour_ls[5])) +
scale_fill_manual(values = c("MutualistBoth"= colour_ls[1], "MutualistHorizontal"= colour_ls[2], "MutualistVertical" = colour_ls[3],"ParasiteBoth"= colour_ls[4], "ParasiteHorizontal" = colour_ls[5])) +
scale_y_discrete(labels = c("MutualistBoth" = "Mutualist-\nBoth", "MutualistHorizontal" = "Mutualist-\nHorizontal","MutualistVertical" = "Mutualist-\nVertical", "ParasiteBoth" = "Parasite-\nBoth", "ParasiteHorizontal" = "Parasite-\nHorizontal")) +
annotate('text', x = 0.93, y = 1:5 + 0.15, label= paste("italic(k)==", res_symbiosis_transmission1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0,1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_mutualism), xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_both), xmin = -0.9, xmax = -0.6, ymin = 0.4, ymax = 1.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -1.1, xmax = -0.9, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_horizontal),xmin = -0.9, xmax = -0.6, ymin = 1.4, ymax = 2.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -1.1, xmax = -0.9, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_vertical), xmin = -0.9, xmax = -0.6, ymin = 2.4, ymax = 3.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -1.1, xmax = -0.9, ymin = 3.6, ymax = 4.2) +
annotation_custom(rasterGrob(image_both), xmin = -0.9, xmax = -0.6, ymin = 3.4, ymax = 4.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -1.1, xmax = -0.9, ymin = 4.6, ymax = 5.2) +
annotation_custom(rasterGrob(image_horizontal), xmin = -0.9, xmax = -0.6, ymin = 4.4, ymax = 5.2)
fig_symbiosis_transmission
## fig 3
g <- ggplot(data = res_symbiosis_transmission1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(symbiosis_transmission)),
aes(x= tanh(Zr), y = symbiosis_transmission, size = ((1/VZr) + 3), colour = symbiosis_transmission), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("MutualistBoth"= colour_ls[1], "MutualistHorizontal"= colour_ls[2], "MutualistVertical" = colour_ls[3],"ParasiteBoth"= colour_ls[4], "ParasiteHorizontal" = colour_ls[5])) +
scale_fill_manual(values = c("MutualistBoth"= colour_ls[1], "MutualistHorizontal"= colour_ls[2], "MutualistVertical" = colour_ls[3],"ParasiteBoth"= colour_ls[4], "ParasiteHorizontal" = colour_ls[5])) +
scale_y_discrete(labels = c("MutualistBoth" = "Mutualist-\nBoth", "MutualistHorizontal" = "Mutualist-\nHorizontal","MutualistVertical" = "Mutualist-\nVertical", "ParasiteBoth" = "Parasite-\nBoth", "ParasiteHorizontal" = "Parasite-\nHorizontal")) +
annotate('text', x = 0.93, y = 1:5 + 0.15, label= paste("italic(k)==", res_symbiosis_transmission1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ,tag = "g" ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position="none") +
theme(axis.text.y = element_text(size = 10, colour ="black",hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_mutualism), xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_both), xmin = -0.9, xmax = -0.6, ymin = 0.4, ymax = 1.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -1.1, xmax = -0.9, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_horizontal),xmin = -0.9, xmax = -0.6, ymin = 1.4, ymax = 2.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -1.1, xmax = -0.9, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_vertical), xmin = -0.9, xmax = -0.6, ymin = 2.4, ymax = 3.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -1.1, xmax = -0.9, ymin = 3.6, ymax = 4.2) +
annotation_custom(rasterGrob(image_both), xmin = -0.9, xmax = -0.6, ymin = 3.4, ymax = 4.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -1.1, xmax = -0.9, ymin = 4.6, ymax = 5.2) +
annotation_custom(rasterGrob(image_horizontal), xmin = -0.9, xmax = -0.6, ymin = 4.4, ymax = 5.2)
Figure 3g: A forest plot showing the group-wise means (the categorical variable symbiosis_transmission) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
# building fig 3 using patchwork
fig3 <- (a/b/c/d + plot_layout(heights = c(1.6, 2, 3.7, 3.7))) | (e/f/g + plot_layout(heights = c(2.8,
2.8, 4.4))) #+ plot_annotation(tag_levels = 'a', tag_suffix = ')')
fig3
# ggsave('../figs/fig3.png', width = 14, height = 14)
# ggsave('../figs/fig3.pdf', width = 14, height = 14)
Figure 3: putting all 7 panels together: Figure 3a - Figure 3g (see the main text)
These are extra analyses not discussed in the main text.
# reordering
dat$host_tax_symbiosis <- factor(dat$host_tax_symbiosis, levels = c("MicrobeMutualist",
"MicrobeParasite", "PlantMutualist", "PlantParasite", "InvertMutualist",
"InvertParasite", "VertMutualist", "VertParasite"))
# meta-regression: mutiple intercepts
mr_host_tax_symbiosis1 <- rma.mv(yi = Zr, V = VZr, mods = ~host_tax_symbiosis -
1, test = "t", random = ~1 | authors, data = dat)
# # meta-regression: contrasts x 10 getting the level names out
level_names <- levels(dat$host_tax_symbiosis)
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = Zr, V = VZr, mods = ~relevel(host_tax_symbiosis, ref = name),
test = "t", random = ~1 | authors, data = dat)
}
# results of meta-regression including all contrast results; taking the last
# level out ([-length(level_names)])
mr_host_tax_symbiosis <- map(level_names[-length(level_names)], run_rma)
Supplementary Table 10: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with host_tax_symbiosis.
# getting marginal R2
r2_host_tax_symbiosis1 <- R2(mr_host_tax_symbiosis1)
# getting estimates
res_host_tax_symbiosis1 <- get_est(mr_host_tax_symbiosis1, mod = "host_tax_symbiosis")
res_host_tax_symbiosis <- map(mr_host_tax_symbiosis, ~get_est(.x, mod = "host_tax_symbiosis"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from = 1, to = 1:7)
# you need to flatten twice: first to make it a list and make it a vector
estimates <- map2(res_host_tax_symbiosis, contra_list, ~.x[-(.y), "estimate"]) %>%
flatten() %>% flatten_dbl()
lowerCLs <- map2(res_host_tax_symbiosis, contra_list, ~.x[-(.y), "lowerCL"]) %>%
flatten() %>% flatten_dbl()
upperCLs <- map2(res_host_tax_symbiosis, contra_list, ~.x[-(.y), "upperCL"]) %>%
flatten() %>% flatten_dbl()
# creating a table
tibble(`Fixed effect` = c(as.character(res_host_tax_symbiosis1$name), cont_gen(res_host_tax_symbiosis1$name)),
Estimate = c(res_host_tax_symbiosis1$estimate, estimates), `Lower CI [0.025]` = c(res_host_tax_symbiosis1$lowerCL,
lowerCLs), `Upper CI [0.975]` = c(res_host_tax_symbiosis1$upperCL,
upperCLs), `V[authors]` = c(mr_host_tax_symbiosis1$sigma2, rep(NA, (8 +
choose(8, 2)) - 1)), R2 = c(r2_host_tax_symbiosis1[1], rep(NA, (8 +
choose(8, 2)) - 1))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left") %>% scroll_box(width = "100%", height = "300px")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| MicrobeMutualist | 0.806 | 0.596 | 1.015 | 0.027 | 0.296 |
| MicrobeParasite | 0.286 | 0.072 | 0.500 | NA | NA |
| PlantMutualist | 0.241 | 0.129 | 0.354 | NA | NA |
| PlantParasite | 0.246 | 0.155 | 0.337 | NA | NA |
| InvertMutualist | 0.424 | 0.348 | 0.500 | NA | NA |
| InvertParasite | 0.348 | 0.210 | 0.486 | NA | NA |
| VertMutualist | 0.674 | 0.143 | 1.206 | NA | NA |
| VertParasite | 0.321 | 0.272 | 0.371 | NA | NA |
| MicrobeMutualist-MicrobeParasite | -0.520 | -0.819 | -0.220 | NA | NA |
| MicrobeMutualist-PlantMutualist | -0.565 | -0.802 | -0.327 | NA | NA |
| MicrobeMutualist-PlantParasite | -0.560 | -0.788 | -0.332 | NA | NA |
| MicrobeMutualist-InvertMutualist | -0.382 | -0.604 | -0.159 | NA | NA |
| MicrobeMutualist-InvertParasite | -0.458 | -0.708 | -0.207 | NA | NA |
| MicrobeMutualist-VertMutualist | -0.131 | -0.703 | 0.440 | NA | NA |
| MicrobeMutualist-VertParasite | -0.484 | -0.699 | -0.269 | NA | NA |
| MicrobeParasite-PlantMutualist | -0.045 | -0.287 | 0.197 | NA | NA |
| MicrobeParasite-PlantParasite | -0.040 | -0.272 | 0.193 | NA | NA |
| MicrobeParasite-InvertMutualist | 0.138 | -0.089 | 0.366 | NA | NA |
| MicrobeParasite-InvertParasite | 0.062 | -0.193 | 0.317 | NA | NA |
| MicrobeParasite-VertMutualist | 0.389 | -0.185 | 0.962 | NA | NA |
| MicrobeParasite-VertParasite | 0.036 | -0.184 | 0.255 | NA | NA |
| PlantMutualist-PlantParasite | 0.005 | -0.131 | 0.141 | NA | NA |
| PlantMutualist-InvertMutualist | 0.183 | 0.047 | 0.319 | NA | NA |
| PlantMutualist-InvertParasite | 0.107 | -0.071 | 0.285 | NA | NA |
| PlantMutualist-VertMutualist | 0.433 | -0.110 | 0.977 | NA | NA |
| PlantMutualist-VertParasite | 0.080 | -0.042 | 0.203 | NA | NA |
| PlantParasite-InvertMutualist | 0.178 | 0.060 | 0.296 | NA | NA |
| PlantParasite-InvertParasite | 0.102 | -0.063 | 0.267 | NA | NA |
| PlantParasite-VertMutualist | 0.428 | -0.111 | 0.968 | NA | NA |
| PlantParasite-VertParasite | 0.075 | -0.028 | 0.179 | NA | NA |
| InvertMutualist-InvertParasite | -0.076 | -0.234 | 0.082 | NA | NA |
| InvertMutualist-VertMutualist | 0.250 | -0.287 | 0.788 | NA | NA |
| InvertMutualist-VertParasite | -0.103 | -0.193 | -0.012 | NA | NA |
| InvertParasite-VertMutualist | 0.326 | -0.223 | 0.876 | NA | NA |
| InvertParasite-VertParasite | -0.027 | -0.170 | 0.116 | NA | NA |
| VertMutualist-VertParasite | -0.353 | -0.887 | 0.181 | NA | NA |
# adding sample size (k) for each category
k_host_tax_symbiosis <- dat %>% group_by(host_tax_symbiosis) %>% count()
# getting estimates and predicitons
pred_host_tax_symbiosis <- get_pred(mr_host_tax_symbiosis1, mod = "host_tax_symbiosis")
res_host_tax_symbiosis1 <- left_join(res_host_tax_symbiosis1, k_host_tax_symbiosis, by = c("name" = "host_tax_symbiosis")) %>% left_join(pred_host_tax_symbiosis)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_host_tax_symbiosis <- ggplot(data = res_host_tax_symbiosis1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(host_tax_symbiosis)),
aes(x= tanh(Zr), y = host_tax_symbiosis, size = ((1/VZr) + 3), colour = host_tax_symbiosis), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("MicrobeMutualist"= colour_ls[1], "MicrobeParasite"= colour_ls[2], "PlantMutualist"= colour_ls[3], "PlantParasite"= colour_ls[4], "InvertMutualist" = colour_ls[5], "InvertParasite"= colour_ls[6], "VertMutualist"= colour_ls[7], "VertParasite"= colour_ls[8] )) +
scale_fill_manual(values = c("MicrobeMutualist"= colour_ls[1], "MicrobeParasite"= colour_ls[2], "PlantMutualist"= colour_ls[3], "PlantParasite"= colour_ls[4], "InvertMutualist" = colour_ls[5], "InvertParasite"= colour_ls[6], "VertMutualist"= colour_ls[7], "VertParasite"= colour_ls[8] )) +
scale_y_discrete(labels = c("MicrobeMutualist"= "Microbe-\nMutualist", "MicrobeParasite"= "Microbe-\nParasite", "PlantMutualist" = "Plant-\nMutualist", "PlantParasite"="Plant-\nParasite", "InvertMutualist" = "Invertebrate-\nMutualist", "InvertParasite"= "Invertebrate-\nParasite", "VertMutualist"= "Vertebrate-\nMutualist", "VertParasite"= "Vertebrate-\nParasite" )) +
annotate('text', x = 0.93, y = 1:8 + 0.15, label= paste("italic(k)==", res_host_tax_symbiosis1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0,1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_microbe_host), xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -0.9, xmax = -0.7, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_microbe_host), xmin = -1.1, xmax = -0.9, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_parasitism),xmin = -0.9, xmax = -0.7, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_plant_host), xmin = -1.1, xmax = -0.9, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -0.9, xmax = -0.7, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_plant_host), xmin = -1.1, xmax = -0.9, ymin = 3.6, ymax = 4.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -0.9, xmax = -0.7, ymin = 3.6, ymax = 4.2) +
annotation_custom(rasterGrob(image_invertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 4.6, ymax = 5.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -0.9, xmax = -0.7, ymin = 4.6, ymax = 5.2) +
annotation_custom(rasterGrob(image_invertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 5.6, ymax = 6.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -0.9, xmax = -0.7, ymin = 5.6, ymax = 6.2) +
annotation_custom(rasterGrob(image_vertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 6.6, ymax = 7.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -0.9, xmax = -0.7, ymin = 6.6, ymax = 7.2) +
annotation_custom(rasterGrob(image_vertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 7.6, ymax = 8.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -0.9, xmax = -0.7, ymin = 7.6, ymax = 8.2)
fig_host_tax_symbiosis
Supplementary Figure 3: A forest plot showing the group-wise means (the categorical variable host_tax_symbiosis) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
Splitting host taxonomy by mode of symbiosis revealed that the observed higher phylogenetic congruence of host-symbiont cophylogenies involving a microbial host is driven primarily by greater congruence between microbial hosts and mutualist symbionts. Congruence is also relatively high for invertebrate hosts that harbour a mutualistic symbiont, while congruence appears to be lowest for plant hosts that harbour a parasitic symbiont.
# reordering
dat$symbiont_tax_symbiosis <- factor(dat$symbiont_tax_symbiosis, levels = c("MicrobeMutualist",
"MicrobeParasite", "PlantMutualist", "PlantParasite", "InvertMutualist",
"InvertParasite", "VertParasite"))
# meta-regression: multiple intercepts
mr_symbiont_tax_symbiosis1 <- rma.mv(yi = Zr, V = VZr, mods = ~symbiont_tax_symbiosis -
1, test = "t", random = ~1 | authors, data = dat)
# # meta-regression: contrasts x 10 getting the level names out
level_names <- levels(dat$symbiont_tax_symbiosis)
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = Zr, V = VZr, mods = ~relevel(symbiont_tax_symbiosis, ref = name),
test = "t", random = ~1 | authors, data = dat)
}
# results of meta-regression including all contrast results; taking the last
# level out ([-length(level_names)])
mr_symbiont_tax_symbiosis <- map(level_names[-length(level_names)], run_rma)
Supplementary Table 11: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_symbiosis.
# getting marginal R2
r2_symbiont_tax_symbiosis1 <- R2(mr_symbiont_tax_symbiosis1)
# getting estimates
res_symbiont_tax_symbiosis1 <- get_est(mr_symbiont_tax_symbiosis1, mod = "symbiont_tax_symbiosis")
res_symbiont_tax_symbiosis <- map(mr_symbiont_tax_symbiosis, ~get_est(.x, mod = "symbiont_tax_symbiosis"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from = 1, to = 1:6)
# you need to flatten twice: first to make it a list and make it a vector
estimates <- map2(res_symbiont_tax_symbiosis, contra_list, ~.x[-(.y), "estimate"]) %>%
flatten() %>% flatten_dbl()
lowerCLs <- map2(res_symbiont_tax_symbiosis, contra_list, ~.x[-(.y), "lowerCL"]) %>%
flatten() %>% flatten_dbl()
upperCLs <- map2(res_symbiont_tax_symbiosis, contra_list, ~.x[-(.y), "upperCL"]) %>%
flatten() %>% flatten_dbl()
# creating a table
tibble(`Fixed effect` = c(as.character(res_symbiont_tax_symbiosis1$name), cont_gen(res_symbiont_tax_symbiosis1$name)),
Estimate = c(res_symbiont_tax_symbiosis1$estimate, estimates), `Lower CI [0.025]` = c(res_symbiont_tax_symbiosis1$lowerCL,
lowerCLs), `Upper CI [0.975]` = c(res_symbiont_tax_symbiosis1$upperCL,
upperCLs), `V[authors]` = c(mr_symbiont_tax_symbiosis1$sigma2, rep(NA,
(7 + choose(7, 2)) - 1)), R2 = c(r2_symbiont_tax_symbiosis1[1], rep(NA,
(7 + choose(7, 2)) - 1))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left") %>% scroll_box(width = "100%", height = "300px")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| MicrobeMutualist | 0.437 | 0.365 | 0.509 | 0.029 | 0.263 |
| MicrobeParasite | 0.279 | 0.213 | 0.345 | NA | NA |
| PlantMutualist | 0.651 | 0.348 | 0.953 | NA | NA |
| PlantParasite | 1.499 | 0.555 | 2.443 | NA | NA |
| InvertMutualist | 0.290 | 0.165 | 0.415 | NA | NA |
| InvertParasite | 0.325 | 0.269 | 0.380 | NA | NA |
| VertParasite | 0.291 | -0.100 | 0.681 | NA | NA |
| MicrobeMutualist-MicrobeParasite | -0.158 | -0.256 | -0.061 | NA | NA |
| MicrobeMutualist-PlantMutualist | 0.214 | -0.097 | 0.525 | NA | NA |
| MicrobeMutualist-PlantParasite | 1.062 | 0.115 | 2.008 | NA | NA |
| MicrobeMutualist-InvertMutualist | -0.147 | -0.291 | -0.002 | NA | NA |
| MicrobeMutualist-InvertParasite | -0.112 | -0.203 | -0.022 | NA | NA |
| MicrobeMutualist-VertParasite | -0.146 | -0.543 | 0.251 | NA | NA |
| MicrobeParasite-PlantMutualist | 0.372 | 0.062 | 0.682 | NA | NA |
| MicrobeParasite-PlantParasite | 1.220 | 0.274 | 2.166 | NA | NA |
| MicrobeParasite-InvertMutualist | 0.011 | -0.130 | 0.153 | NA | NA |
| MicrobeParasite-InvertParasite | 0.046 | -0.040 | 0.132 | NA | NA |
| MicrobeParasite-VertParasite | 0.012 | -0.384 | 0.408 | NA | NA |
| PlantMutualist-PlantParasite | 0.848 | -0.143 | 1.839 | NA | NA |
| PlantMutualist-InvertMutualist | -0.361 | -0.688 | -0.033 | NA | NA |
| PlantMutualist-InvertParasite | -0.326 | -0.634 | -0.019 | NA | NA |
| PlantMutualist-VertParasite | -0.360 | -0.854 | 0.134 | NA | NA |
| PlantParasite-InvertMutualist | -1.208 | -2.161 | -0.256 | NA | NA |
| PlantParasite-InvertParasite | -1.174 | -2.120 | -0.228 | NA | NA |
| PlantParasite-VertParasite | -1.208 | -2.229 | -0.186 | NA | NA |
| InvertMutualist-InvertParasite | 0.034 | -0.099 | 0.167 | NA | NA |
| InvertMutualist-VertParasite | 0.001 | -0.409 | 0.411 | NA | NA |
| InvertParasite-VertParasite | -0.034 | -0.428 | 0.361 | NA | NA |
# adding sample size (k) for each category
k_symbiont_tax_symbiosis <- dat %>% group_by(symbiont_tax_symbiosis) %>% count()
# getting estimates and predicitons
pred_symbiont_tax_symbiosis <- get_pred(mr_symbiont_tax_symbiosis1, mod = "symbiont_tax_symbiosis")
res_symbiont_tax_symbiosis1 <- left_join(res_symbiont_tax_symbiosis1, k_symbiont_tax_symbiosis, by = c("name" = "symbiont_tax_symbiosis")) %>% left_join(pred_symbiont_tax_symbiosis)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_symbiont_tax_symbiosis <- ggplot(data = res_symbiont_tax_symbiosis1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(symbiont_tax_symbiosis)),
aes(x= tanh(Zr), y = symbiont_tax_symbiosis, size = ((1/VZr) + 3), colour = symbiont_tax_symbiosis), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
# setting colours
scale_color_manual(values = c("MicrobeMutualist"= colour_ls[1], "MicrobeParasite"= colour_ls[2], "PlantMutualist"= colour_ls[3], "PlantParasite"= colour_ls[4], "InvertMutualist" = colour_ls[5], "InvertParasite"= colour_ls[6], "VertParasite"= colour_ls[8] )) +
scale_fill_manual(values = c("MicrobeMutualist"= colour_ls[1], "MicrobeParasite"= colour_ls[2], "PlantMutualist"= colour_ls[3], "PlantParasite"= colour_ls[4], "InvertMutualist" = colour_ls[5], "InvertParasite"= colour_ls[6], "VertParasite"= colour_ls[8] )) +
scale_y_discrete(labels = c("MicrobeMutualist"= "Microbe-\nMutualist", "MicrobeParasite"= "Microbe-\nParasite", "PlantMutualist" = "Plant-\nMutualist", "PlantParasite"="Plant-\nParasite", "InvertMutualist" = "Invertebrate-\nMutualist", "InvertParasite"= "Invertebrate-\nParasite", "VertParasite"= "Vertebrate-\nParasite" )) +
annotate('text', x = 0.93, y = 1:7 + 0.15, label= paste("italic(k)==", res_symbiont_tax_symbiosis1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_microbe_parasite), xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -0.9, xmax = -0.7, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_microbe_parasite), xmin = -1.1, xmax = -0.9, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_parasitism),xmin = -0.9, xmax = -0.7, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_plant_parasite), xmin = -1.1, xmax = -0.9, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -0.9, xmax = -0.7, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_plant_parasite), xmin = -1.1, xmax = -0.9, ymin = 3.6, ymax = 4.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -0.9, xmax = -0.7, ymin = 3.6, ymax = 4.2) +
annotation_custom(rasterGrob(image_invertebrate_parasite), xmin = -1.1, xmax = -0.9, ymin = 4.6, ymax = 5.2) +
annotation_custom(rasterGrob(image_mutualism), xmin = -0.9, xmax = -0.7, ymin = 4.6, ymax = 5.2) +
annotation_custom(rasterGrob(image_invertebrate_parasite), xmin = -1.1, xmax = -0.9, ymin = 5.6, ymax = 6.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -0.9, xmax = -0.7, ymin = 5.6, ymax = 6.2) +
annotation_custom(rasterGrob(image_vertebrate_parasite), xmin = -1.1, xmax = -0.9, ymin = 6.6, ymax = 7.2) +
annotation_custom(rasterGrob(image_parasitism), xmin = -0.9, xmax = -0.7, ymin = 6.6, ymax = 7.2)
fig_symbiont_tax_symbiosis
Supplementary Figure 4: A forest plot showing the group-wise means (the categorical variable symbiont_tax_symbiosis) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
Splitting symbiont taxonomy by mode of symbiosis revealed much less variation, except for higher congruence exhibited by cophylogenies involving a plant symbiont (which are relatively rare), and the finding that cophylogenies involving a microbial mutualist symbiont are slightly more congruent than the remaining categories.
# reordering
dat$host_symbiont_tax <- factor(dat$host_symbiont_tax, levels = c("MicrobeInvert",
"MicrobeMicrobe", "MicrobePlant", "PlantInvert", "PlantMicrobe", "InvertInvert",
"InvertMicrobe", "InvertPlant", "VertInvert", "VertMicrobe", "VertVert"))
# meta-regression: multiple intercepts
mr_host_symbiont_tax1 <- rma.mv(yi = Zr, V = VZr, mods = ~host_symbiont_tax -
1, test = "t", random = ~1 | authors, data = dat)
# # meta-regression: contrasts x 10 getting the level names out
level_names <- levels(dat$host_symbiont_tax)
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = Zr, V = VZr, mods = ~relevel(host_symbiont_tax, ref = name),
test = "t", random = ~1 | authors, data = dat)
}
# results of meta-regression including all contrast results; taking the last
# level out ([-length(level_names)])
mr_host_symbiont_tax <- map(level_names[-length(level_names)], run_rma)
Supplementary Table 12: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with host_symbiont_tax.
# getting marginal R2
r2_host_symbiont_tax1 <- R2(mr_host_symbiont_tax1)
# getting estimates
res_host_symbiont_tax1 <- get_est(mr_host_symbiont_tax1, mod = "host_symbiont_tax")
res_host_symbiont_tax <- map(mr_host_symbiont_tax, ~get_est(.x, mod = "host_symbiont_tax"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from = 1, to = 1:10)
# you need to flatten twice: first to make it a list and make it a vector
estimates <- map2(res_host_symbiont_tax, contra_list, ~.x[-(.y), "estimate"]) %>%
flatten() %>% flatten_dbl()
lowerCLs <- map2(res_host_symbiont_tax, contra_list, ~.x[-(.y), "lowerCL"]) %>%
flatten() %>% flatten_dbl()
upperCLs <- map2(res_host_symbiont_tax, contra_list, ~.x[-(.y), "upperCL"]) %>%
flatten() %>% flatten_dbl()
# creating a table
tibble(`Fixed effect` = c(as.character(res_host_symbiont_tax1$name), cont_gen(res_host_symbiont_tax1$name)),
Estimate = c(res_host_symbiont_tax1$estimate, estimates), `Lower CI [0.025]` = c(res_host_symbiont_tax1$lowerCL,
lowerCLs), `Upper CI [0.975]` = c(res_host_symbiont_tax1$upperCL, upperCLs),
`V[authors]` = c(mr_host_tax_symbiosis1$sigma2, rep(NA, (11 + choose(11,
2)) - 1)), R2 = c(r2_host_symbiont_tax1[1], rep(NA, (11 + choose(11,
2)) - 1))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left") %>% scroll_box(width = "100%", height = "300px")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| MicrobeInvert | 0.623 | -0.119 | 1.366 | 0.027 | 0.22 |
| MicrobeMicrobe | 0.508 | 0.328 | 0.689 | NA | NA |
| MicrobePlant | 0.696 | 0.379 | 1.012 | NA | NA |
| PlantInvert | 0.216 | 0.119 | 0.312 | NA | NA |
| PlantMicrobe | 0.295 | 0.167 | 0.423 | NA | NA |
| InvertInvert | 0.421 | 0.286 | 0.556 | NA | NA |
| InvertMicrobe | 0.405 | 0.325 | 0.485 | NA | NA |
| InvertPlant | 0.929 | 0.187 | 1.672 | NA | NA |
| VertInvert | 0.355 | 0.287 | 0.422 | NA | NA |
| VertMicrobe | 0.293 | 0.215 | 0.371 | NA | NA |
| VertVert | 0.291 | -0.107 | 0.688 | NA | NA |
| MicrobeInvert-MicrobeMicrobe | -0.115 | -0.879 | 0.649 | NA | NA |
| MicrobeInvert-MicrobePlant | 0.072 | -0.735 | 0.880 | NA | NA |
| MicrobeInvert-PlantInvert | -0.408 | -1.157 | 0.341 | NA | NA |
| MicrobeInvert-PlantMicrobe | -0.328 | -1.082 | 0.425 | NA | NA |
| MicrobeInvert-InvertInvert | -0.202 | -0.957 | 0.553 | NA | NA |
| MicrobeInvert-InvertMicrobe | -0.218 | -0.965 | 0.529 | NA | NA |
| MicrobeInvert-InvertPlant | 0.306 | -0.744 | 1.356 | NA | NA |
| MicrobeInvert-VertInvert | -0.269 | -1.014 | 0.477 | NA | NA |
| MicrobeInvert-VertMicrobe | -0.330 | -1.077 | 0.417 | NA | NA |
| MicrobeInvert-VertVert | -0.332 | -1.174 | 0.510 | NA | NA |
| MicrobeMicrobe-MicrobePlant | 0.187 | -0.177 | 0.552 | NA | NA |
| MicrobeMicrobe-PlantInvert | -0.293 | -0.498 | -0.088 | NA | NA |
| MicrobeMicrobe-PlantMicrobe | -0.213 | -0.435 | 0.008 | NA | NA |
| MicrobeMicrobe-InvertInvert | -0.087 | -0.313 | 0.139 | NA | NA |
| MicrobeMicrobe-InvertMicrobe | -0.103 | -0.301 | 0.095 | NA | NA |
| MicrobeMicrobe-InvertPlant | 0.421 | -0.343 | 1.185 | NA | NA |
| MicrobeMicrobe-VertInvert | -0.154 | -0.347 | 0.039 | NA | NA |
| MicrobeMicrobe-VertMicrobe | -0.215 | -0.412 | -0.018 | NA | NA |
| MicrobeMicrobe-VertVert | -0.217 | -0.654 | 0.219 | NA | NA |
| MicrobePlant-PlantInvert | -0.480 | -0.811 | -0.149 | NA | NA |
| MicrobePlant-PlantMicrobe | -0.401 | -0.742 | -0.059 | NA | NA |
| MicrobePlant-InvertInvert | -0.274 | -0.618 | 0.069 | NA | NA |
| MicrobePlant-InvertMicrobe | -0.290 | -0.617 | 0.036 | NA | NA |
| MicrobePlant-InvertPlant | 0.234 | -0.574 | 1.041 | NA | NA |
| MicrobePlant-VertInvert | -0.341 | -0.665 | -0.018 | NA | NA |
| MicrobePlant-VertMicrobe | -0.403 | -0.728 | -0.077 | NA | NA |
| MicrobePlant-VertVert | -0.405 | -0.913 | 0.103 | NA | NA |
| PlantInvert-PlantMicrobe | 0.080 | -0.081 | 0.240 | NA | NA |
| PlantInvert-InvertInvert | 0.206 | 0.040 | 0.372 | NA | NA |
| PlantInvert-InvertMicrobe | 0.190 | 0.064 | 0.315 | NA | NA |
| PlantInvert-InvertPlant | 0.714 | -0.035 | 1.463 | NA | NA |
| PlantInvert-VertInvert | 0.139 | 0.021 | 0.257 | NA | NA |
| PlantInvert-VertMicrobe | 0.078 | -0.047 | 0.202 | NA | NA |
| PlantInvert-VertVert | 0.075 | -0.334 | 0.485 | NA | NA |
| PlantMicrobe-InvertInvert | 0.126 | -0.060 | 0.312 | NA | NA |
| PlantMicrobe-InvertMicrobe | 0.110 | -0.041 | 0.261 | NA | NA |
| PlantMicrobe-InvertPlant | 0.634 | -0.119 | 1.388 | NA | NA |
| PlantMicrobe-VertInvert | 0.059 | -0.085 | 0.204 | NA | NA |
| PlantMicrobe-VertMicrobe | -0.002 | -0.152 | 0.148 | NA | NA |
| PlantMicrobe-VertVert | -0.004 | -0.422 | 0.413 | NA | NA |
| InvertInvert-InvertMicrobe | -0.016 | -0.173 | 0.141 | NA | NA |
| InvertInvert-InvertPlant | 0.508 | -0.247 | 1.263 | NA | NA |
| InvertInvert-VertInvert | -0.067 | -0.211 | 0.078 | NA | NA |
| InvertInvert-VertMicrobe | -0.128 | -0.284 | 0.028 | NA | NA |
| InvertInvert-VertVert | -0.130 | -0.550 | 0.290 | NA | NA |
| InvertMicrobe-InvertPlant | 0.524 | -0.223 | 1.271 | NA | NA |
| InvertMicrobe-VertInvert | -0.051 | -0.156 | 0.054 | NA | NA |
| InvertMicrobe-VertMicrobe | -0.112 | -0.224 | 0.000 | NA | NA |
| InvertMicrobe-VertVert | -0.114 | -0.520 | 0.291 | NA | NA |
| InvertPlant-VertInvert | -0.575 | -1.320 | 0.171 | NA | NA |
| InvertPlant-VertMicrobe | -0.636 | -1.383 | 0.111 | NA | NA |
| InvertPlant-VertVert | -0.638 | -1.481 | 0.204 | NA | NA |
| VertInvert-VertMicrobe | -0.061 | -0.165 | 0.042 | NA | NA |
| VertInvert-VertVert | -0.064 | -0.467 | 0.340 | NA | NA |
| VertMicrobe-VertVert | -0.002 | -0.407 | 0.403 | NA | NA |
# colour list
#colour_ls <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E422", "#0072B2", "#D55E00", "#CC79A7", "#00008B", "#8B0A50", "#54FF9F", "#999999")
# adding sample size (k) for each category
k_host_symbiont_tax <- dat %>% group_by(host_symbiont_tax) %>% count()
# getting estimates and predicitons
pred_host_symbiont_tax <- get_pred(mr_host_symbiont_tax1, mod = "host_symbiont_tax")
res_host_symbiont_tax1 <- left_join(res_host_symbiont_tax1, k_host_symbiont_tax, by = c("name" = "host_symbiont_tax")) %>% left_join(pred_host_symbiont_tax)
#res_symbiosis1
# drawing a funnel plot - fig 2b
fig_host_symbiont_tax <- ggplot(data = res_host_symbiont_tax1, aes(x = tanh(estimate), y = name)) +
scale_x_continuous(limits=c(-1, 1), breaks = seq(-1, 1, by = 0.2) ) +
geom_quasirandom(data = dat %>% filter(!is.na(host_symbiont_tax)),
aes(x= tanh(Zr), y = host_symbiont_tax, size = ((1/VZr) + 3), colour = host_symbiont_tax), groupOnX = FALSE, alpha=0.4) +
# 95 %precition interval (PI)
geom_errorbarh(aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
# 95 %CI
geom_errorbarh(aes(xmin = tanh(lowerCL), xmax = tanh(upperCL)), height = 0, show.legend = F, size = 1.2) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
# creating dots and different size (bee-swarm and bubbles)
geom_point(aes(fill = name), size = 3, shape = 21) + #
# setting colours
scale_color_manual(values = c("MicrobeInvert" = colour_ls[1], "MicrobeMicrobe"= colour_ls[2], "MicrobePlant" = colour_ls[3], "PlantInvert" = colour_ls[4],"PlantMicrobe" = colour_ls[5], "InvertInvert" = colour_ls[6], "InvertMicrobe" = colour_ls[7], "InvertPlant" = colour_ls[8],"VertInvert" = colour_ls[9], "VertMicrobe"= colour_ls[10],"VertVert" = colour_ls[11])) +
scale_fill_manual(values = c("MicrobeInvert" = colour_ls[1], "MicrobeMicrobe"= colour_ls[2], "MicrobePlant" = colour_ls[3], "PlantInvert" = colour_ls[4],"PlantMicrobe" = colour_ls[5], "InvertInvert" = colour_ls[6], "InvertMicrobe" = colour_ls[7], "InvertPlant" = colour_ls[8],"VertInvert" = colour_ls[9], "VertMicrobe"= colour_ls[10],"VertVert" = colour_ls[11])) +
scale_y_discrete(labels = c("MicrobeInvert" = "Microbe-\nInvertebrate", "MicrobeMicrobe"= "Microbe-\nMicrobe", "MicrobePlant" = "Microbe-\nPlant", "PlantInvert" = "Plant-\nInvertebrate","PlantMicrobe" = "Plant-\nMicrobe", "InvertInvert" = "Invertebrate\nInvertebrate", "InvertMicrobe" = "Invertebrate-\nMicrobe", "InvertPlant" = "Invertebrate-\nPlant","VertInvert" = "Vertebrate-\nInvertebrate", "VertMicrobe"= "Vertebrate-\nMicrobe", "VertVert" = "Vertebrate-\nVertebrate")) +
annotate('text', x = 0.93, y = 1:11 + 0.15, label= paste("italic(k)==", res_host_symbiont_tax1$n), parse=TRUE, hjust = "left", size=3.5) +
labs(x = expression(paste(italic(r), " (correlation)")), y = "", size = expression(paste(italic(n), " (# of species)")) ) +
guides(fill = "none", colour = "none") +
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0,1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90)) +
# putting pictures in
annotation_custom(rasterGrob(image_microbe_host), xmin = -1.1, xmax = -0.9, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_invertebrate_parasite), xmin = -0.9, xmax = -0.7, ymin = 0.6, ymax = 1.2) +
annotation_custom(rasterGrob(image_microbe_host), xmin = -1.1, xmax = -0.9, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_microbe_parasite),xmin = -0.9, xmax = -0.7, ymin = 1.6, ymax = 2.2) +
annotation_custom(rasterGrob(image_microbe_host), xmin = -1.1, xmax = -0.9, ymin = 2.6, ymax = 3.2) +
annotation_custom(rasterGrob(image_plant_parasite), xmin = -0.9, xmax = -0.7, ymin = 2.6, ymax = 3.2) +
#
annotation_custom(rasterGrob(image_plant_host), xmin = -1.1, xmax = -0.9, ymin = 3.6, ymax = 4.2) +
annotation_custom(rasterGrob(image_invertebrate_parasite), xmin = -0.9, xmax = -0.7, ymin = 3.6, ymax = 4.2) +
annotation_custom(rasterGrob(image_plant_host), xmin = -1.1, xmax = -0.9, ymin = 4.6, ymax = 5.2) +
annotation_custom(rasterGrob(image_microbe_parasite), xmin = -0.9, xmax = -0.7, ymin = 4.6, ymax = 5.2) +
#
annotation_custom(rasterGrob(image_invertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 5.6, ymax = 6.2) +
annotation_custom(rasterGrob(image_invertebrate_parasite), xmin = -0.9, xmax = -0.7, ymin = 5.6, ymax = 6.2) +
annotation_custom(rasterGrob(image_invertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 6.6, ymax = 7.2) +
annotation_custom(rasterGrob(image_microbe_parasite), xmin = -0.9, xmax = -0.7, ymin = 6.6, ymax = 7.2) +
annotation_custom(rasterGrob(image_invertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 7.6, ymax = 8.2) +
annotation_custom(rasterGrob(image_plant_parasite), xmin = -0.9, xmax = -0.7, ymin = 7.6, ymax = 8.2) +
#
annotation_custom(rasterGrob(image_vertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 8.6, ymax = 9.2) +
annotation_custom(rasterGrob(image_invertebrate_parasite), xmin = -0.9, xmax = -0.7, ymin = 8.6, ymax = 9.2) +
annotation_custom(rasterGrob(image_vertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 9.6, ymax = 10.2) +
annotation_custom(rasterGrob(image_microbe_parasite), xmin = -0.9, xmax = -0.7, ymin = 9.6, ymax = 10.2) +
annotation_custom(rasterGrob(image_vertebrate_host), xmin = -1.1, xmax = -0.9, ymin = 10.6, ymax = 11.2) +
annotation_custom(rasterGrob(image_vertebrate_parasite), xmin = -0.9, xmax = -0.7, ymin = 10.6, ymax = 11.2)
fig_host_symbiont_tax
Supplementary Figure 4: A forest plot showing the group-wise means (the categorical variable host_symbiont_tax) with their 95% confidences interval (thick lines) and 95% prediction intervals (thin lines) with observed effect sizes based on various sample sizes.
Here we build the best model via an AICc based model selection method implemented in the R package MuMin10. For the full model, we had 6 variables: symbiosis, host_tax_broad, symbiont_tax_broad, mode_of_transmission_broad, endo_or_ecto, & log(host_range_link_ratio). We did not use log(host_range_taxonomic_breadth) as it is co-linear with log(host_range_link_ratio) and also any of interaction terms.
# creates a new function to run in MuMIn
updated.rma.mv <- updateable(rma.mv)
# updated.rma.mv
# testing the new function use method = 'ML' so that we can compare AIC
mr_full <- updated.rma.mv(yi = Zr, V = VZr, mods = ~symbiosis + host_tax_broad +
symbiont_tax_broad + mode_of_transmission_broad + endo_or_ecto + log(host_range_link_ratio),
test = "t", random = ~1 | authors, method = "ML", data = dat)
# ============================= additional methods for 'rma.mv' class (made
# by Kamil Barton) we need this to run model selection with rma.mv in MuMIn
# =============================
formula.rma.mv <- function(x, ...) return(eval(getCall(x)$mods))
makeArgs.rma.mv <- function(obj, termNames, comb, opt, ...) {
ret <- MuMIn:::makeArgs.default(obj, termNames, comb, opt)
names(ret)[1L] <- "mods"
ret
}
nobs.rma.mv <- function(object, ...) attr(logLik(object), "nall")
coefTable.rma.mv <- function(model, ...) MuMIn:::.makeCoefTable(model$b, model$se,
coefNames = rownames(model$b))
# =============================
# testing dredge dredge(full.model, evaluate=F) # show all candidate models
# n = 32 model exisit
candidates <- dredge(mr_full)
# displays delta AICc <2
candidates_aic2 <- subset(candidates, delta < 2)
# model averaging it seems like models are using z values rather than t
# values (which will be OK)
mr_averaged_aic2 <- summary(model.avg(candidates, delta < 2))
# relative importance of each predictor
importance <- importance(candidates)
# use REML if not for model comparision
model1 <- rma.mv(yi = Zr, V = VZr, mods = ~mode_of_transmission_broad + host_tax_broad,
test = "t", random = ~1 | authors, method = "REML", data = dat)
model2 <- rma.mv(yi = Zr, V = VZr, mods = ~mode_of_transmission_broad + host_tax_broad +
symbiosis, test = "t", random = ~1 | authors, method = "REML", data = dat)
Supplementary Table 13: The top 2 models (out of 32 possible models) within the \(\Delta\)AIC difference of 2 and which 6 variables: symbiosis, host_tax_broad, symbiont_tax_broad, mode_of_transmission_broad, endo_or_ecto, & log(host_range_link_ratio) were included (indicated by \(+\)); model weights (for the 2 models) and the sum of weights for each of the variable (from the 32 models) are included.
# creating a table
tibble(`Model (variable weight)` = c("Model1", "Model2", "(Sum of weights)"),
transmission = c(if_else(candidates_aic2$mode_of_transmission_broad == "+",
"$+$", "NA"), round(importance[1], 3)), host_tax = c(if_else(candidates_aic2$host_tax_broad ==
"+", "$+$", "NA"), round(importance[2], 3)), symbiosis = c(if_else(candidates_aic2$symbiosis ==
"+", "$+$", "NA"), round(importance[3], 3)), symbiont_tax = c(if_else(candidates_aic2$symbiont_tax_broad ==
"+", "$+$", "NA"), round(importance[4], 3)), endo_or_ecto = c(if_else(candidates_aic2$endo_or_ecto ==
"+", "$+$", "NA"), round(importance[5], 3)), host_range = c(if_else(candidates_aic2$`log(host_range_link_ratio)` ==
"+", "$+$", "NA"), round(importance[6], 3)), delta_AICc = c(candidates_aic2$delta,
NA), Weight = c(candidates_aic2$weight, NA)) %>% kable("html", digits = 3) %>%
kable_styling("striped", position = "left")
| Model (variable weight) | transmission | host_tax | symbiosis | symbiont_tax | endo_or_ecto | host_range | delta_AICc | Weight |
|---|---|---|---|---|---|---|---|---|
| Model1 | \(+\) | \(+\) | NA | NA | NA | NA | 0.000 | 0.633 |
| Model2 | \(+\) | \(+\) | \(+\) | NA | NA | NA | 1.087 | 0.367 |
| (Sum of weights) | 0.998 | 0.682 | 0.342 | 0.217 | 0.129 | 0.087 | NA | NA |
Supplementary Table 14: The averaged estimates for regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the 2 best meta-regression models.
# getting averaged R2 and variance components not provided by the MuMIn
# package
average_sigma2 <- weighted.mean(x = c(model1$sigma2, model2$sigma2), w = candidates_aic2$weight)
average_R2 <- weighted.mean(x = c(R2(model1)[1], R2(model2)[1]), w = candidates_aic2$weight)
# creating a table
tibble(`Fixed effect` = c("Intercept (both-Microbe-Mutulist)", "Microbe-Plant",
"Microbe-Invert", "Microbe-Vert", "both-horizontal", "both-vertical", "Mutulist-Parasite"),
Estimate = mr_averaged_aic2$coefmat.full[, 1], `Lower CI [0.025]` = mr_averaged_aic2$coefmat.full[,
1] - mr_averaged_aic2$coefmat.full[, 2] * qnorm(0.975), `Upper CI [0.975]` = mr_averaged_aic2$coefmat.full[,
1] + mr_averaged_aic2$coefmat.full[, 2] * qnorm(0.975), `V[authors]` = c(average_sigma2,
rep(NA, 6)), R2 = c(average_R2, rep(NA, 6))) %>% kable("html", digits = 3) %>%
kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Intercept (both-Microbe-Mutulist) | 0.547 | 0.382 | 0.711 | 0.024 | 0.282 |
| Microbe-Plant | -0.248 | -0.417 | -0.078 | NA | NA |
| Microbe-Invert | -0.185 | -0.355 | -0.015 | NA | NA |
| Microbe-Vert | -0.173 | -0.332 | -0.014 | NA | NA |
| both-horizontal | -0.055 | -0.141 | 0.032 | NA | NA |
| both-vertical | 0.120 | -0.031 | 0.271 | NA | NA |
| Mutulist-Parasite | -0.016 | -0.095 | 0.062 | NA | NA |
Here, we conducted 3 kinds of publication bias analyses: 1) contour-enhanced funnel plots11 of residuals7,12, 2) a type of Egger regression12,13, and 3) a regression-based time-lag bias test7.
A normal funnel plot assumes the homogeneity (i.e., I2 = 0). therefore, we controlled for important moderators (i.e., mode_of_transmission_broad, host_tax_broad, & symbiosis).
It is not normal skewness that we see in our enhanced-counter funnel plot (Supplementary Figure 5). This funnel asymmetry seems different from one cased by publication bias11; we do not expect a “hollow” in the region with high precision or i.e. inverse standard error (4.5-6) and relative high effect sizes, (Zr = 0.2-0.7). The funnel asymmetry is mainly caused by the boundary created (see “Sensitivity Analysis” section where we deal with this skewness).
#
res_funnel_plot <- rma.mv(yi = Zr, V = VZr, mods = ~mode_of_transmission_broad +
host_tax_broad + symbiosis, random = ~1 | authors, data = dat)
funnel(res_funnel_plot, yaxis = "seinv", level = c(90, 95, 99), shade = c("white",
"gray55", "gray75"), refline = 0, legend = TRUE)
Supplementary Figure 5: A residual funnel plot from the meta-regression model with mode_of_transmission_broad, host_tax_broad, & symbiosis; ‘residual value’ is on Zr and ‘inverse standard error’ is precision 1/sqrt(VZr).
Further, Egger regression analyses (see below) showed sqrt(VZr) (sampling errors [SE] for effect sizes) accounts much heterogeneity so that we added to our model. The funnel asymmetry we see in Supplementary Figure 6 (if any) is much less sever than that in Supplementary Figure 5.
#
res_funnel_plot2 <- rma.mv(yi = Zr, V = VZr, mods = ~sqrt(VZr) + mode_of_transmission_broad +
host_tax_broad + symbiosis, random = ~1 | authors, data = dat)
funnel(res_funnel_plot2, yaxis = "seinv", level = c(90, 95, 99), shade = c("white",
"gray55", "gray75"), refline = 0, legend = TRUE)
Supplementary Figure 6: A residual funnel plot from the meta-regression model with sqrt(VZr), mode_of_transmission_broad, host_tax_broad, & symbiosis; ‘residual value’ is on Zr and ‘inverse standard error’ is precision 1/sqrt(VZr).
Egger regression tests whether funnel asymmetries we see in funnel plots are statistical significant or not.
The test (or sqrt(VZr)) is significant. However, as mentioned above, this is due to the boundary created by the number of randamizations; this boundary can be seen in Supplementary Figure 7
#
egger_regression_uni <- rma.mv(yi = Zr, V = VZr, mods = ~sqrt(VZr), random = ~1 |
authors, data = dat)
Supplementary Table 15: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# getting marginal R2
r2_egger_regression_uni <- R2(egger_regression_uni)
# getting estimates: name does not work for slopes
res_egger_regression_uni <- get_est(egger_regression_uni, mod = "sqrt(VZr)")
# creating a table
tibble(`Fixed effect` = c("Intercept", "sqrt(VZr)"), Estimate = c(res_egger_regression_uni$estimate),
`Lower CI [0.025]` = c(res_egger_regression_uni$lowerCL), `Upper CI [0.975]` = c(res_egger_regression_uni$upperCL),
`V[authors]` = c(egger_regression_uni$sigma2, NA), R2 = c(r2_egger_regression_uni[1],
NA)) %>% kable("html", digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Intercept | 0.124 | 0.035 | 0.212 | 0.026 | 0.255 |
| sqrt(VZr) | 1.314 | 0.817 | 1.810 | NA | NA |
pred_egger_regression_uni <- predict.rma(egger_regression_uni)
# plotting
fit_egger_regression_uni <- dat %>% mutate(ymin = pred_egger_regression_uni$ci.lb,
ymax = pred_egger_regression_uni$ci.ub, ymin2 = pred_egger_regression_uni$cr.lb,
ymax2 = pred_egger_regression_uni$cr.ub, pred = pred_egger_regression_uni$pred) %>%
ggplot(aes(x = sqrt(VZr), y = Zr, size = (1/VZr) + 3)) + geom_point(shape = 21,
fill = "grey90") + # geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = '#0072B2') + # not quite
# sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25,
colour = "#0072B2") + geom_smooth(aes(y = ymax2), method = "loess", se = FALSE,
lty = "dotted", lwd = 0.25, colour = "#0072B2") + geom_smooth(aes(y = ymin),
method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted",
lwd = 0.25, colour = "#D55E00") + geom_smooth(aes(y = pred), method = "loess",
se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") + ylim(-1, 2) +
xlim(0.05, 0.45) + # geom_abline(intercept = mr_host_range_link_ratio$beta[[1]], slope =
# mr_host_range_link_ratio$beta[[2]], alpha = 0.7, linetype = 'dashed', size
# = 0.5) +
labs(x = "sqrt(sampling variance)", y = expression(paste(italic(Zr), " (effect size)")),
size = expression(paste(italic(n), " (# of species)"))) + guides(fill = "none",
colour = "none") + # themses
theme_bw() + theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction = "horizontal") + # theme(legend.background = element_rect(fill = 'white', colour = 'black'))
# +
theme(legend.background = element_blank()) + theme(axis.text.y = element_text(size = 10,
colour = "black", hjust = 0.5, angle = 90))
fit_egger_regression_uni
Supplementary Figure 7: A bubble plot showing a predicted regression line for the contentious variable log(host_range_link_ratio) with their 95% confidences regions (orange dotted lines) and 95% prediction regions (blue dotted lines) with observed effect sizes based on various sample sizes.
We also conducted a Egger regression controlling other important moderators (i.e., mode_of_transmission_broad, host_tax_broad, & symbiosis). After controlling for these variables, sqrt(VZr) stays significant.
#
egger_regression_mul <- rma.mv(yi = Zr, V = VZr, mods = ~sqrt(VZr) + mode_of_transmission_broad +
host_tax_broad + symbiosis, random = ~1 | authors, data = dat)
Supplementary Table 16: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# getting marginal R2
r2_egger_regression_mul <- R2(egger_regression_mul)
# creating a table
tibble(`Fixed effect` = c("Intercept (both-Microbe-Mutulist)", "sqrt(VZr)",
"both-horizontal", "both-vertical", "Microbe-Plant", "Microbe-Invert", "Microbe-Vert",
"Mutulist-Parasite"), Estimate = c(egger_regression_mul$b), `Lower CI [0.025]` = c(egger_regression_mul$ci.lb),
`Upper CI [0.975]` = c(egger_regression_mul$ci.ub), `V[authors]` = c(egger_regression_mul$sigma2,
rep(NA, 7)), R2 = c(r2_egger_regression_mul[1], rep(NA, 7))) %>% kable("html",
digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Intercept (both-Microbe-Mutulist) | 0.290 | 0.094 | 0.486 | 0.018 | 0.478 |
| sqrt(VZr) | 1.202 | 0.726 | 1.678 | NA | NA |
| both-horizontal | -0.043 | -0.125 | 0.039 | NA | NA |
| both-vertical | 0.138 | -0.016 | 0.291 | NA | NA |
| Microbe-Plant | -0.167 | -0.331 | -0.002 | NA | NA |
| Microbe-Invert | -0.145 | -0.309 | 0.019 | NA | NA |
| Microbe-Vert | -0.095 | -0.251 | 0.062 | NA | NA |
| Mutulist-Parasite | -0.040 | -0.144 | 0.065 | NA | NA |
pred_egger_regression_mul <-predict.rma(egger_regression_mul)
# plotting
fit_egger_regression_mul <- dat %>%
filter(!is.na(mode_of_transmission_broad) & !is.na(host_tax_broad) & !is.na(symbiosis)) %>% # getting ride of NA values
mutate(ymin = pred_egger_regression_mul$ci.lb,
ymax = pred_egger_regression_mul$ci.ub,
ymin2 = pred_egger_regression_mul$cr.lb,
ymax2 = pred_egger_regression_mul$cr.ub,
pred = pred_egger_regression_mul$pred) %>%
ggplot(aes(x = sqrt(VZr), y = Zr, size = (1/VZr) + 3)) +
geom_point(shape = 21, fill = "grey90") +
#geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2") + # not quite sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymin), method = "loess", se = FALSE,lty = "dotted", lwd = 0.25, colour ="#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty ="dotted", lwd = 0.25, colour ="#D55E00") +
geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty ="dashed", lwd = 0.5, colour ="black") +
ylim(-1, 2) + xlim(0.05, 0.45) +
#geom_abline(intercept = mr_host_range_link_ratio$beta[[1]], slope = mr_host_range_link_ratio$beta[[2]], alpha = 0.7, linetype = "dashed", size = 0.5) +
labs(x = "sqrt(sampling variance)", y = expression(paste(italic(Zr), " (effect size)")), size = expression(paste(italic(n), " (# of species)"))) +
guides(fill = "none", colour = "none") +
# themses
theme_bw() +
theme(legend.position= c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction="horizontal") +
#theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour ="black", hjust = 0.5, angle = 90))
fit_egger_regression_mul
Supplementary Figure 8: A bubble plot showing a predicted loess line for the contentious variable log(host_range_link_ratio) (given the values of the other 3 variables in the model) with their 95% confidences regions (orange dotted lines) and 95% prediction regions (blue dotted lines) with observed effect sizes based on various sample sizes. Note that the lines are not linear as these are based on multivariate predictions of the data points.
We do not find any evidence of the time-lag effect (a decline in the magnitude of the effect over time) in both the univariate and multivariate models (Supplementary Figure 9 and Supplementary Figure 10).
#
time_lag_effect_uni <- rma.mv(yi = Zr, V = VZr, mods = ~year, random = ~1 |
authors, data = dat)
Supplementary Table 17: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with year.
# getting marginal R2
r2_time_lag_effect_uni <- R2(time_lag_effect_uni)
# getting estimates: name does not work for slopes
res_time_lag_effect_uni <- get_est(time_lag_effect_uni, mod = "year")
# creating a table
tibble(`Fixed effect` = c("Intercept", "Year"), Estimate = c(res_time_lag_effect_uni$estimate),
`Lower CI [0.025]` = c(res_time_lag_effect_uni$lowerCL), `Upper CI [0.975]` = c(res_time_lag_effect_uni$upperCL),
`V[authors]` = c(time_lag_effect_uni$sigma2, NA), R2 = c(r2_time_lag_effect_uni[1],
NA)) %>% kable("html", digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Intercept | -2.547 | -14.985 | 9.892 | 0.033 | 0.002 |
| Year | 0.001 | -0.005 | 0.008 | NA | NA |
pred_time_lag_effect_uni <- predict.rma(time_lag_effect_uni)
# plotting
fit_time_lag_effect <- dat %>% mutate(ymin = pred_time_lag_effect_uni$ci.lb,
ymax = pred_time_lag_effect_uni$ci.ub, ymin2 = pred_time_lag_effect_uni$cr.lb,
ymax2 = pred_time_lag_effect_uni$cr.ub, pred = pred_time_lag_effect_uni$pred) %>%
ggplot(aes(x = year, y = Zr, size = (1/VZr) + 3)) + geom_point(shape = 21,
fill = "grey90") + # geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = '#0072B2') + # not quite
# sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25,
colour = "#0072B2") + geom_smooth(aes(y = ymax2), method = "loess", se = FALSE,
lty = "dotted", lwd = 0.25, colour = "#0072B2") + geom_smooth(aes(y = ymin),
method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted",
lwd = 0.25, colour = "#D55E00") + geom_smooth(aes(y = pred), method = "loess",
se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") + ylim(-1, 2) +
xlim(1994, 2019) + scale_x_continuous(breaks = c(1995, 2000, 2005, 2010,
2015, 2020)) + # geom_abline(intercept = mr_host_range_link_ratio$beta[[1]], slope =
# mr_host_range_link_ratio$beta[[2]], alpha = 0.7, linetype = 'dashed', size
# = 0.5) +
labs(x = "Year", y = expression(paste(italic(Zr), " (effect size)")), size = expression(paste(italic(n),
" (# of species)"))) + guides(fill = "none", colour = "none") + # themses
theme_bw() + theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction = "horizontal") + # theme(legend.background = element_rect(fill = 'white', colour = 'black'))
# +
theme(legend.background = element_blank()) + theme(axis.text.y = element_text(size = 10,
colour = "black", hjust = 0.5, angle = 90))
fit_time_lag_effect
Supplementary Figure 9: A bubble plot showing a predicted regression line for the contentious variable year with their 95% confidences regions (orange dotted lines) and 95% prediction regions (blue dotted lines) with observed effect sizes based on various sample sizes.
#
time_lag_effect_mul <- rma.mv(yi = Zr, V = VZr, mods = ~year + mode_of_transmission_broad +
host_tax_broad + symbiosis, random = ~1 | authors, data = dat)
Supplementary Table 18: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with year.
# getting marginal R2
r2_time_lag_effect_mul <- R2(time_lag_effect_mul)
# creating a table
tibble(`Fixed effect` = c("Intercept (both-Microbe-Mutulist)", "Year", "both-horizontal",
"both-vertical", "Microbe-Plant", "Microbe-Invert", "Microbe-Vert", "Mutulist-Parasite"),
Estimate = c(time_lag_effect_mul$b), `Lower CI [0.025]` = c(time_lag_effect_mul$ci.lb),
`Upper CI [0.975]` = c(time_lag_effect_mul$ci.ub), `V[authors]` = c(time_lag_effect_mul$sigma2,
rep(NA, 7)), R2 = c(r2_time_lag_effect_mul[1], rep(NA, 7))) %>% kable("html",
digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Intercept (both-Microbe-Mutulist) | 1.221 | -10.447 | 12.890 | 0.024 | 0.28 |
| Year | 0.000 | -0.006 | 0.005 | NA | NA |
| both-horizontal | -0.053 | -0.141 | 0.036 | NA | NA |
| both-vertical | 0.101 | -0.064 | 0.265 | NA | NA |
| Microbe-Plant | -0.255 | -0.428 | -0.082 | NA | NA |
| Microbe-Invert | -0.187 | -0.361 | -0.014 | NA | NA |
| Microbe-Vert | -0.168 | -0.333 | -0.003 | NA | NA |
| Mutulist-Parasite | -0.045 | -0.156 | 0.067 | NA | NA |
pred_time_lag_effect_mul <- predict.rma(time_lag_effect_mul)
# plotting
fit_time_lag_effect_mul <- dat %>% filter(!is.na(mode_of_transmission_broad) &
!is.na(host_tax_broad) & !is.na(symbiosis)) %>% mutate(ymin = pred_time_lag_effect_mul$ci.lb,
ymax = pred_time_lag_effect_mul$ci.ub, ymin2 = pred_time_lag_effect_mul$cr.lb,
ymax2 = pred_time_lag_effect_mul$cr.ub, pred = pred_time_lag_effect_mul$pred) %>%
ggplot(aes(x = year, y = Zr, size = (1/VZr) + 3)) + geom_point(shape = 21,
fill = "grey90") + # geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = '#0072B2') + # not quite
# sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25,
colour = "#0072B2") + geom_smooth(aes(y = ymax2), method = "loess", se = FALSE,
lty = "dotted", lwd = 0.25, colour = "#0072B2") + geom_smooth(aes(y = ymin),
method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted",
lwd = 0.25, colour = "#D55E00") + geom_smooth(aes(y = pred), method = "loess",
se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") + ylim(-1, 2) +
xlim(1994, 2019) + scale_x_continuous(breaks = c(1995, 2000, 2005, 2010,
2015, 2020)) + # geom_abline(intercept = mr_host_range_link_ratio$beta[[1]], slope =
# mr_host_range_link_ratio$beta[[2]], alpha = 0.7, linetype = 'dashed', size
# = 0.5) +
labs(x = "Year", y = expression(paste(italic(Zr), " (effect size)")), size = expression(paste(italic(n),
" (# of species)"))) + guides(fill = "none", colour = "none") + # themses
theme_bw() + theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction = "horizontal") + # theme(legend.background = element_rect(fill = 'white', colour = 'black'))
# +
theme(legend.background = element_blank()) + theme(axis.text.y = element_text(size = 10,
colour = "black", hjust = 0.5, angle = 90))
fit_time_lag_effect_mul
Supplementary Figure 10: A bubble plot showing a predicted loess line for the contentious variable year (given the values of the other 3 variables in the model) with their 95% confidences regions (orange dotted lines) and 95% prediction regions (blue dotted lines) with observed effect sizes based on various sample sizes. Note that the lines are not linear as these are based on multivariate predictions of the data points.
The funnel plots above identified the issue of upper bounds of this effect size given a sample size (a upper limit of a p value given the number of randomizations). This boundary would make our estimates of mean effect sizes and contrasts (i.e., comparing two groups) so that our our overall conclusions are often conservative. To demonstrate this, we conducted two analyses to show: 1) the number of randomizations do not differ between categories in the 3 important categorical moderators (mode_of_transmission_broad, host_tax_broad, & symbiosis) and 2) categories with high effect sizes would include “bounded” effect sizes (i.e., from p = 0.01, 0.001, or 0.0001) in the 3 moderators.
Below, we showed that none of categorizes have significantly different numbers of randomizations in all mode_of_transmission_broad, host_tax_broad, & symbiosis.
# 233 --- Yes = 74 (0.3175966%); No = 159
# symbiosis multiple intercepts
sa_random_symbiosis1 <- lmer(log(no_randomizations) ~ symbiosis - 1 + (1 | authors),
data = dat)
# contrast
sa_random_symbiosis2 <- lmer(log(no_randomizations) ~ symbiosis + (1 | authors),
data = dat)
Supplementary Table 19: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# getting marginal R2
r2_sa_random_symbiosis <- r2_nakagawa(sa_random_symbiosis1)
# getting estimates
res_sa_random_symbiosis <- tibble(estiamte = c(fixef(sa_random_symbiosis1),
fixef(sa_random_symbiosis2)[2]))
ci_sa_random_symbiosis1 <- confint(sa_random_symbiosis1)
ci_sa_random_symbiosis2 <- confint(sa_random_symbiosis2)
res_sa_random_symbiosis %<>% mutate(lowerCL = c(ci_sa_random_symbiosis1[3:4,
1], ci_sa_random_symbiosis2[4, 1]))
res_sa_random_symbiosis %<>% mutate(upperCL = c(ci_sa_random_symbiosis1[3:4,
2], ci_sa_random_symbiosis2[4, 2]))
# creating a table
tibble(`Fixed effect` = c(as.character(res_symbiosis1$name), cont_gen(res_symbiosis1$name)),
Estimate = res_sa_random_symbiosis$estiamte, `Lower CI [0.025]` = res_sa_random_symbiosis$lowerCL,
`Upper CI [0.975]` = res_sa_random_symbiosis$upperCL, `V[authors]` = c(attr(VarCorr(sa_random_symbiosis1)$author,
"stddev")^2, rep(NA, 2)), `V[residuals]` = c(attr(VarCorr(sa_random_symbiosis1),
"sc")^2, rep(NA, 2)), R2 = c(r2_sa_random_symbiosis$R2_marginal, rep(NA,
2))) %>% kable("html", digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | V[residuals] | R2 |
|---|---|---|---|---|---|---|
| Mutualist | 7.696 | 7.286 | 8.106 | 2.931 | 0.352 | 0.004 |
| Parasite | 7.619 | 7.314 | 7.924 | NA | NA | NA |
| Mutualist-Parasite | -0.077 | -0.544 | 0.389 | NA | NA | NA |
# host_tax_broad mutiple intercepts
sa_random_host_tax_broad1 <- lmer(log(no_randomizations) ~ host_tax_broad -
1 + (1 | authors), data = dat)
# contrast 1
sa_random_host_tax_broad2 <- lmer(log(no_randomizations) ~ host_tax_broad +
(1 | authors), data = dat)
# contrast 2
sa_random_host_tax_broad3 <- lmer(log(no_randomizations) ~ relevel(host_tax_broad,
ref = "Plant") + (1 | authors), data = dat)
# contrast 3
sa_random_host_tax_broad4 <- lmer(log(no_randomizations) ~ relevel(host_tax_broad,
ref = "Invert") + (1 | authors), data = dat)
Supplementary Table 20: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# getting marginal R2
r2_sa_random_host_tax_broad <- r2_nakagawa(sa_random_host_tax_broad1)
# getting estimates
res_sa_random_host_tax_broad <- tibble(estiamte = c(fixef(sa_random_host_tax_broad1),
fixef(sa_random_host_tax_broad2)[2:4],
fixef(sa_random_host_tax_broad3)[3:4],
fixef(sa_random_host_tax_broad4)[4]))
ci_sa_random_host_tax_broad1<-confint(sa_random_host_tax_broad1)
ci_sa_random_host_tax_broad2<-confint(sa_random_host_tax_broad2)
ci_sa_random_host_tax_broad3<-confint(sa_random_host_tax_broad3)
ci_sa_random_host_tax_broad4<-confint(sa_random_host_tax_broad4)
res_sa_random_host_tax_broad %<>% mutate(lowerCL = c(ci_sa_random_host_tax_broad1[3:6,1],
ci_sa_random_host_tax_broad2[4:6,1],
ci_sa_random_host_tax_broad3[5:6,1],
ci_sa_random_host_tax_broad4[6,1]))
res_sa_random_host_tax_broad %<>% mutate(upperCL = c(ci_sa_random_host_tax_broad1[3:6,2],
ci_sa_random_host_tax_broad2[4:6,2],
ci_sa_random_host_tax_broad3[5:6,2],
ci_sa_random_host_tax_broad4[6,2]))
# creating a table
tibble(
`Fixed effect` = c(as.character(res_symbiont_tax_broad1$name), cont_gen(res_symbiont_tax_broad1$name)), # done
Estimate = res_sa_random_host_tax_broad$estiamte,
`Lower CI [0.025]` = res_sa_random_host_tax_broad$lowerCL,
`Upper CI [0.975]` = res_sa_random_host_tax_broad$upperCL,
`V[authors]` = c(attr(VarCorr(sa_random_host_tax_broad1)$author,"stddev")^2, rep(NA, 9)),
`V[residuals]` =c(attr(VarCorr(sa_random_host_tax_broad1),"sc")^2, rep(NA, 9)),
`R2` = c(r2_sa_random_host_tax_broad$R2_marginal, rep(NA, 9))) %>% kable("html", digits = 3) %>%
kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | V[residuals] | R2 |
|---|---|---|---|---|---|---|
| Microbe | 8.223 | 7.279 | 9.168 | 2.937 | 0.35 | 0.071 |
| Plant | 7.466 | 6.856 | 8.076 | NA | NA | NA |
| Invert | 7.650 | 7.167 | 8.134 | NA | NA | NA |
| Vert | 7.634 | 7.265 | 8.003 | NA | NA | NA |
| Microbe-Plant | -0.757 | -1.882 | 0.367 | NA | NA | NA |
| Microbe-Invert | -0.573 | -1.634 | 0.488 | NA | NA | NA |
| Microbe-Vert | -0.589 | -1.603 | 0.425 | NA | NA | NA |
| Plant-Invert | 0.184 | -0.594 | 0.962 | NA | NA | NA |
| Plant-Vert | 0.168 | -0.545 | 0.881 | NA | NA | NA |
| Invert-Vert | -0.016 | -0.599 | 0.566 | NA | NA | NA |
# mode_of_transmission_broad
sa_random_mode_of_transmission_broad1 <- lmer(log(no_randomizations) ~ mode_of_transmission_broad -
1 + (1 | authors), data = dat)
# contrast 1
sa_random_mode_of_transmission_broad2 <- lmer(log(no_randomizations) ~ mode_of_transmission_broad +
(1 | authors), data = dat)
# contrast 2
sa_random_mode_of_transmission_broad3 <- lmer(log(no_randomizations) ~ relevel(mode_of_transmission_broad,
ref = "vertical") + (1 | authors), data = dat)
Supplementary Table 21: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# getting marginal R2
r2_sa_random_mode_of_transmission_broad <- r2_nakagawa(sa_random_mode_of_transmission_broad1)
# getting estimates
res_sa_random_mode_of_transmission_broad <- tibble(estiamte = c(fixef(sa_random_mode_of_transmission_broad1),
fixef(sa_random_mode_of_transmission_broad2)[2:3], fixef(sa_random_mode_of_transmission_broad3)[3]))
ci_sa_random_mode_of_transmission_broad1 <- confint(sa_random_mode_of_transmission_broad1)
ci_sa_random_mode_of_transmission_broad2 <- confint(sa_random_mode_of_transmission_broad2)
ci_sa_random_mode_of_transmission_broad3 <- confint(sa_random_mode_of_transmission_broad3)
res_sa_random_mode_of_transmission_broad %<>% mutate(lowerCL = c(ci_sa_random_mode_of_transmission_broad1[3:5,
1], ci_sa_random_mode_of_transmission_broad2[4:5, 1], ci_sa_random_mode_of_transmission_broad3[5,
1]))
res_sa_random_mode_of_transmission_broad %<>% mutate(upperCL = c(ci_sa_random_mode_of_transmission_broad1[3:5,
2], ci_sa_random_mode_of_transmission_broad2[4:5, 2], ci_sa_random_mode_of_transmission_broad3[5,
2]))
# creating a table
tibble(`Fixed effect` = c(as.character(res_mode_of_transmission_broad1$name),
cont_gen(res_mode_of_transmission_broad1$name)), Estimate = res_sa_random_mode_of_transmission_broad$estiamte,
`Lower CI [0.025]` = res_sa_random_mode_of_transmission_broad$lowerCL, `Upper CI [0.975]` = res_sa_random_mode_of_transmission_broad$upperCL,
`V[authors]` = c(attr(VarCorr(sa_random_mode_of_transmission_broad1)$author,
"stddev")^2, rep(NA, 5)), `V[residuals]` = c(attr(VarCorr(sa_random_mode_of_transmission_broad1),
"sc")^2, rep(NA, 5)), R2 = c(r2_sa_random_mode_of_transmission_broad$R2_marginal,
rep(NA, 5))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | V[residuals] | R2 |
|---|---|---|---|---|---|---|
| both | 7.535 | 6.978 | 8.091 | 2.913 | 0.363 | 0.12 |
| horizontal | 7.515 | 7.164 | 7.866 | NA | NA | NA |
| vertical | 8.089 | 7.509 | 8.668 | NA | NA | NA |
| both-horizontal | -0.020 | -0.678 | 0.638 | NA | NA | NA |
| both-vertical | 0.554 | -0.250 | 1.357 | NA | NA | NA |
| horizontal-vertical | -0.574 | -1.251 | 0.104 | NA | NA | NA |
Below, we showed that categorizes with higher effect sizes were more likely to have “bounded” effect sizes (limit_rearched) in all mode_of_transmission_broad, host_tax_broad, & symbiosis. This indicate that the higher the estimate of mean effect size is, the more underestimated the mean effect size is. This is true for differences between two categories; the larger the difference between two, the more underestimated the difference is.
Supplementary Table 22: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# symbiosis
sa_limit_symbiosis1 <- glmer(limit_rearched ~ symbiosis - 1 + (1 | authors),
family = "binomial", data = dat)
# getting marginal R2
r2_sa_limit_symbiosis <- r2_nakagawa(sa_limit_symbiosis1)
# getting estimates
res_sa_limit_symbiosis <- tibble(estiamte = fixef(sa_limit_symbiosis1))
res_sa_limit_symbiosis %<>% mutate(lowerCL = (tidy(sa_limit_symbiosis1)$estimate[-3] -
tidy(sa_limit_symbiosis1)$std.error[-3] * qnorm(0.975)))
res_sa_limit_symbiosis %<>% mutate(upperCL = (tidy(sa_limit_symbiosis1)$estimate[-3] +
tidy(sa_limit_symbiosis1)$std.error[-3] * qnorm(0.975)))
# creating a table
tibble(`Fixed effect` = as.character(res_symbiosis1$name), Estimate = res_sa_limit_symbiosis$estiamte,
`Lower CI [0.025]` = res_sa_limit_symbiosis$lowerCL, `Upper CI [0.975]` = res_sa_limit_symbiosis$upperCL,
`V[authors]` = c(attr(VarCorr(sa_limit_symbiosis1)$author, "stddev")^2,
rep(NA, 1)), R2 = c(r2_sa_limit_symbiosis$R2_marginal, rep(NA, 1))) %>%
kable("html", digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Mutualist | -0.401 | -1.074 | 0.272 | 1.657 | 0.051 |
| Parasite | -1.309 | -2.015 | -0.603 | NA | NA |
Supplementary Table 23: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# host_tax_broad
sa_limit_host_tax_broad1 <- glmer(limit_rearched ~ host_tax_broad - 1 + (1 |
authors), family = "binomial", data = dat)
# getting marginal R2
r2_sa_limit_host_tax_broad <- r2_nakagawa(sa_limit_host_tax_broad1)
# getting estimates
res_sa_limit_host_tax_broad <- tibble(estiamte = fixef(sa_limit_host_tax_broad1))
res_sa_limit_host_tax_broad %<>% mutate(lowerCL = (tidy(sa_limit_host_tax_broad1)$estimate[-5] -
tidy(sa_limit_host_tax_broad1)$std.error[-5] * qnorm(0.975)))
res_sa_limit_host_tax_broad %<>% mutate(upperCL = (tidy(sa_limit_host_tax_broad1)$estimate[-5] +
tidy(sa_limit_host_tax_broad1)$std.error[-5] * qnorm(0.975)))
# creating a table
tibble(`Fixed effect` = as.character(res_symbiont_tax_broad1$name), Estimate = res_sa_limit_host_tax_broad$estiamte,
`Lower CI [0.025]` = res_sa_limit_host_tax_broad$lowerCL, `Upper CI [0.975]` = res_sa_limit_host_tax_broad$upperCL,
`V[authors]` = c(attr(VarCorr(sa_limit_host_tax_broad1)$author, "stddev")^2,
rep(NA, 3)), R2 = c(r2_sa_limit_host_tax_broad$R2_marginal, rep(NA,
3))) %>% kable("html", digits = 3) %>% kable_styling("striped", position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| Microbe | -1.211 | -2.737 | 0.316 | 1.358 | 0.035 |
| Plant | -1.574 | -2.571 | -0.578 | NA | NA |
| Invert | -0.579 | -1.308 | 0.150 | NA | NA |
| Vert | -0.937 | -1.588 | -0.286 | NA | NA |
Supplementary Table 24: Regression coefficients (Estimate), 95% confidence intervals (CIs), variance components (V) and variance explained, R2[marginal] (R2) from the meta-regression with symbiont_tax_broad.
# mode_of_transmission_broad
sa_limit_mode_of_transmission_broad1 <- glmer(limit_rearched ~ mode_of_transmission_broad -
1 + (1 | authors), family = "binomial", data = dat)
# getting marginal R2
r2_sa_limit_mode_of_transmission_broad <- r2_nakagawa(sa_limit_mode_of_transmission_broad1)
# getting estimates
res_sa_limit_mode_of_transmission_broad <- tibble(estiamte = fixef(sa_limit_mode_of_transmission_broad1))
res_sa_limit_mode_of_transmission_broad %<>% mutate(lowerCL = (tidy(sa_limit_mode_of_transmission_broad1)$estimate[-4] -
tidy(sa_limit_mode_of_transmission_broad1)$std.error[-4] * qnorm(0.975)))
res_sa_limit_mode_of_transmission_broad %<>% mutate(upperCL = (tidy(sa_limit_mode_of_transmission_broad1)$estimate[-4] +
tidy(sa_limit_mode_of_transmission_broad1)$std.error[-4] * qnorm(0.975)))
# creating a table
tibble(`Fixed effect` = as.character(res_mode_of_transmission_broad1$name),
Estimate = res_sa_limit_mode_of_transmission_broad$estiamte, `Lower CI [0.025]` = res_sa_limit_mode_of_transmission_broad$lowerCL,
`Upper CI [0.975]` = res_sa_limit_mode_of_transmission_broad$upperCL, `V[authors]` = c(attr(VarCorr(sa_limit_mode_of_transmission_broad1)$author,
"stddev")^2, rep(NA, 2)), R2 = c(r2_sa_limit_mode_of_transmission_broad$R2_marginal,
rep(NA, 2))) %>% kable("html", digits = 3) %>% kable_styling("striped",
position = "left")
| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | V[authors] | R2 |
|---|---|---|---|---|---|
| both | -0.928 | -1.809 | -0.046 | 1.427 | 0.078 |
| horizontal | -1.326 | -2.031 | -0.621 | NA | NA |
| vertical | 0.060 | -0.750 | 0.869 | NA | NA |
We have 3 recommendations for future (and past) co-divergence work using TreeMap and ParaFit.
Many coding materials have been borrowed from these paper14,15. We thank Losia Lagisz for preparing small icons and cartoons used in the figures.
sessionInfo() %>% pander()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
locale: en_AU.UTF-8||en_AU.UTF-8||en_AU.UTF-8||C||en_AU.UTF-8||en_AU.UTF-8
attached base packages: grid, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: patchwork(v.0.0.1), png(v.0.1-7), performance(v.0.3.0), broom.mixed(v.0.2.4), lme4(v.1.1-21), MuMIn(v.1.43.6), plotly(v.4.9.0), ggbeeswarm(v.0.6.0), MCMCglmm(v.2.29), ape(v.5.3), coda(v.0.19-3), metafor(v.2.1-0), Matrix(v.1.2-17), pander(v.0.6.3), magrittr(v.1.5), gridExtra(v.2.3), kableExtra(v.1.1.0), forcats(v.0.4.0), stringr(v.1.4.0), dplyr(v.0.8.3), purrr(v.0.3.2), readr(v.1.3.1), tidyr(v.0.8.3), tibble(v.2.1.3), ggplot2(v.3.2.1) and tidyverse(v.1.2.1)
loaded via a namespace (and not attached): httr(v.1.4.1), jsonlite(v.1.6), viridisLite(v.0.3.0), splines(v.3.6.1), modelr(v.0.1.5), assertthat(v.0.2.1), highr(v.0.8), stats4(v.3.6.1), tensorA(v.0.36.1), vipor(v.0.4.5), cellranger(v.1.1.0), bayestestR(v.0.2.5), yaml(v.2.2.0), pillar(v.1.4.2), backports(v.1.1.4), lattice(v.0.20-38), glue(v.1.3.1), digest(v.0.6.20), rvest(v.0.3.4), minqa(v.1.2.4), colorspace(v.1.4-1), plyr(v.1.8.4), htmltools(v.0.3.6), pkgconfig(v.2.0.2), broom(v.0.5.2), haven(v.2.1.1), corpcor(v.1.6.9), scales(v.1.0.0), webshot(v.0.5.1), cubature(v.2.0.3), generics(v.0.0.2), pacman(v.0.5.1), withr(v.2.1.2), TMB(v.1.7.15), lazyeval(v.0.2.2), cli(v.1.1.0), crayon(v.1.3.4), readxl(v.1.3.1), evaluate(v.0.14), nlme(v.3.1-141), MASS(v.7.3-51.4), xml2(v.1.2.2), beeswarm(v.0.2.3), tools(v.3.6.1), data.table(v.1.12.2), hms(v.0.5.0), formatR(v.1.7), munsell(v.0.5.0), compiler(v.3.6.1), rlang(v.0.4.0), nloptr(v.1.2.1), rstudioapi(v.0.10.0-9000), htmlwidgets(v.1.3), labeling(v.0.3), base64enc(v.0.1-3), rmarkdown(v.1.14), boot(v.1.3-23), codetools(v.0.2-16), gtable(v.0.3.0), reshape2(v.1.4.3), R6(v.2.4.0), lubridate(v.1.7.4), knitr(v.1.24), zeallot(v.0.1.0), insight(v.0.4.1), stringi(v.1.4.3), parallel(v.3.6.1), Rcpp(v.1.0.2), vctrs(v.0.2.0), tidyselect(v.0.2.5) and xfun(v.0.8)
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